1. The document discusses automatic recognition of emotions in speech, including models and methods. It covers topics like affective factors in human-machine interaction, speech and multimodal sensor data, discrete and dimensional descriptions of affect, and software for affect annotation.
2. It describes technical steps for affect annotation and automatic emotion recognition, and evaluates different data used. Potential applications are discussed in companion systems and dialog control.
3. The document provides details on recognizing emotions indirectly through speech, gestures, expressions and other modalities, and the technical challenges involved. It also reviews different models for categorizing human emotion that can be used for automatic recognition.
The document discusses automatic recognition of emotions in speech. It describes technical steps for affect annotation and automatic emotion recognition using software. Emotions are conveyed through speech as well as other modalities like facial expressions and gestures. Raw data from recordings first needs to be transcribed and annotated to tag emotional content before automatic recognition can analyze the data.
Emotion Recognition and Emotional Resonance: Exploring the Relationship betwe...Rebecca Noskeau
This document summarizes a study that explored the relationship between facial expression recognition and empathy. The study found that females scored higher than males on an empathy questionnaire. Scores on the empathy questionnaire were positively correlated with accuracy in identifying facial expressions. Participants were most accurate and resonated most with facial expressions of happiness and the three female facial stimuli. Certain emotions like happiness and surprise were identified more accurately than others like fear and anger. Females recognized anger more accurately than males. The findings support a relationship between empathy and facial expression recognition abilities.
This document presents a human emotion recognition system that uses facial expression analysis to identify emotions. It discusses how emotions are important to human life and interaction. The system first captures images of a human face and preprocesses the images to extract features. It then compares the facial features to examples in a database to recognize the emotion based on distances between features. The system can identify six basic emotions with up to 97% accuracy. Limitations and potential to incorporate fuzzy logic for improved classification are also discussed.
The document proposes developing Android applications to sense emotions using smartphones for better health and human-machine interactions. It discusses detecting emotions through passive sensors like cameras, microphones, and accelerometers that can capture facial expressions, speech, heart rate without interpreting input. Recognition involves extracting meaningful patterns from sensor data using techniques like speech recognition, facial expression detection to produce labels or inference algorithms. Specific techniques are discussed for recognizing emotions from speech, facial expressions based on the Facial Action Coding System, and heart rate variability. The conclusion states that understanding emotions with smartphones can help people succeed and make research easier.
This document contains pictures of children displaying different emotions such as happy, sad, scared, surprised, and more, with clear captions labeling the emotion shown in each picture. The pictures seek to help teach children emotion recognition and understanding through visual examples paired with simple word labels for 12 different emotional states.
The document discusses how facial expressions can be translated into language descriptions. It provides examples of common facial expressions and their meanings, such as a broad smile, a frown, a grimace, a dirty look, raising eyebrows, beaming from ear to ear, having a long face, being purple with rage, and deadly looks.
This document provides an overview of facial recognition systems. It discusses what facial recognition is, which is a computer system that identifies people by analyzing facial features from images and video. It explains that facial recognition started in the 1960s and is now commonly used for security applications. The document outlines different approaches to facial recognition, including two-dimensional and three-dimensional systems, and describes some of the techniques involved like feature extraction and classification. It also provides details about a specific facial recognition product called FA007 and its uses for access control.
Facial expressions are a universal form of non-verbal communication. The human face is capable of over 1000 expressions through movements of muscles around the eyes, eyebrows, mouth, and nose. There are six basic universal facial expressions of emotion: happiness, sadness, surprise, disgust, fear, and anger. Research has shown these expressions to be natural rather than learned, as blind individuals display the same facial expressions as sighted Olympians. However, facial expressions are limited as a form of communication in modern contexts that rely on technology like phones, text, and email, as well as for those with disabilities affecting sight or muscle movement.
The document discusses automatic recognition of emotions in speech. It describes technical steps for affect annotation and automatic emotion recognition using software. Emotions are conveyed through speech as well as other modalities like facial expressions and gestures. Raw data from recordings first needs to be transcribed and annotated to tag emotional content before automatic recognition can analyze the data.
Emotion Recognition and Emotional Resonance: Exploring the Relationship betwe...Rebecca Noskeau
This document summarizes a study that explored the relationship between facial expression recognition and empathy. The study found that females scored higher than males on an empathy questionnaire. Scores on the empathy questionnaire were positively correlated with accuracy in identifying facial expressions. Participants were most accurate and resonated most with facial expressions of happiness and the three female facial stimuli. Certain emotions like happiness and surprise were identified more accurately than others like fear and anger. Females recognized anger more accurately than males. The findings support a relationship between empathy and facial expression recognition abilities.
This document presents a human emotion recognition system that uses facial expression analysis to identify emotions. It discusses how emotions are important to human life and interaction. The system first captures images of a human face and preprocesses the images to extract features. It then compares the facial features to examples in a database to recognize the emotion based on distances between features. The system can identify six basic emotions with up to 97% accuracy. Limitations and potential to incorporate fuzzy logic for improved classification are also discussed.
The document proposes developing Android applications to sense emotions using smartphones for better health and human-machine interactions. It discusses detecting emotions through passive sensors like cameras, microphones, and accelerometers that can capture facial expressions, speech, heart rate without interpreting input. Recognition involves extracting meaningful patterns from sensor data using techniques like speech recognition, facial expression detection to produce labels or inference algorithms. Specific techniques are discussed for recognizing emotions from speech, facial expressions based on the Facial Action Coding System, and heart rate variability. The conclusion states that understanding emotions with smartphones can help people succeed and make research easier.
This document contains pictures of children displaying different emotions such as happy, sad, scared, surprised, and more, with clear captions labeling the emotion shown in each picture. The pictures seek to help teach children emotion recognition and understanding through visual examples paired with simple word labels for 12 different emotional states.
The document discusses how facial expressions can be translated into language descriptions. It provides examples of common facial expressions and their meanings, such as a broad smile, a frown, a grimace, a dirty look, raising eyebrows, beaming from ear to ear, having a long face, being purple with rage, and deadly looks.
This document provides an overview of facial recognition systems. It discusses what facial recognition is, which is a computer system that identifies people by analyzing facial features from images and video. It explains that facial recognition started in the 1960s and is now commonly used for security applications. The document outlines different approaches to facial recognition, including two-dimensional and three-dimensional systems, and describes some of the techniques involved like feature extraction and classification. It also provides details about a specific facial recognition product called FA007 and its uses for access control.
Facial expressions are a universal form of non-verbal communication. The human face is capable of over 1000 expressions through movements of muscles around the eyes, eyebrows, mouth, and nose. There are six basic universal facial expressions of emotion: happiness, sadness, surprise, disgust, fear, and anger. Research has shown these expressions to be natural rather than learned, as blind individuals display the same facial expressions as sighted Olympians. However, facial expressions are limited as a form of communication in modern contexts that rely on technology like phones, text, and email, as well as for those with disabilities affecting sight or muscle movement.
BASIC ANALYSIS ON PROSODIC FEATURES IN EMOTIONAL SPEECHIJCSEA Journal
Speech is a rich source of information which gives not only about what a speaker says, but also about what the speaker’s attitude is toward the listener and toward the topic under discussion—as well as the speaker’s own current state of mind. Recently increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. The focus of this research work is to enhance man machine interface by focusing on user’s speech emotion. This paper gives the results of the basic analysis on prosodic features and also compares the prosodic features
of, various types and degrees of emotional expressions in Tamil speech based on the auditory impressions between the two genders of speakers as well as listeners. The speech samples consist of “neutral” speech as well as speech with three types of emotions (“anger”, “joy”, and “sadness”) of three degrees (“light”, “medium”, and “strong”). A listening test is also being conducted using 300 speech samples uttered by students at the ages of 19 -22 the ages of 19-22 years old. The features of prosodic parameters based on the emotional speech classified according to the auditory impressions of the subjects are analyzed. Analysis results suggest that prosodic features that identify their emotions and degrees are not only speakers’ gender dependent, but also listeners’ gender dependent.
Annotation Of Emotion Carriers In Personal NarrativesErica Thompson
This document describes research on annotating emotion carriers in personal narratives. It defines emotion carriers as speech or text segments that best explain the emotional state of the narrator. The researchers experimented with manually annotating emotion carriers in German personal narratives from the Ulm State-of-Mind in Speech corpus. They believe this annotated resource could be used to automatically extract emotion carriers from personal narratives, advancing understanding of narratives. The task is challenging as the same term may or may not carry emotion depending on context, making the search space for identifying carriers large.
1) Affective computing aims to expand human emotional intelligence to machines by creating socially intelligent machines that can respond appropriately according to the situation and interlocutor.
2) There are two main approaches to modeling emotions in affective computing: discrete theories that identify basic emotions like Ekman's six emotions, and continuous theories that describe emotions along dimensions of arousal and valence.
3) Empath's goal is to recognize emotions from speech regardless of language, which presents challenges of combining speech processing with emotion recognition from voice cues alone. Empath is developing methods to extract pitch, intensity, and speech rate from voice samples to train models to classify emotions.
Abstract: Speech technology and systems in human computer interaction have witnessed a stable and remarkable advancement over the last two decades. Today, speech technologies are commercially available for an unlimited but interesting range of tasks. These technologies enable machines to respond correctly and reliably to human voices, and provide useful and valuable services. This thesis presents the characteristics of emotion in voice and on that basis propose a new method to detecting emotion in a simplified way by using a prosodic features and spectral from speech. We classify seven emotions: happy, anger, fear, disgust, sadness and neutral inner state. This thesis discusses the method to extract features from a recorded speech sample, and using those features, to detect the emotion of the subject. Every emotion comprises different vocal parameters exhibiting diverse characteristics of speech, which is used for preliminary classification. Then Mel-Frequency Cepstrum Coefficient (MFCC) method was used to extract spectral features. The MFCC coefficients were again trained by Artificial Neural Network (ANN) which then classifies the input in particular emotional class.
Facial Expression Recognition System: A Digital Printing Applicationijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Facial Expression Recognition System: A Digital Printing Applicationijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document summarizes a study on the impact of emotion on prosody analysis in speech. The study analyzed speech samples recorded from actors expressing different emotions like love, anger, calm, sadness and neutral. It measured acoustic parameters like vowel duration, fundamental frequency, jitter and shimmer for the different emotions. The results showed that speech expressing love had longer vowel durations, while sad speech had longer durations for certain vowels. This indicates emotion impacts prosodic features of speech, which is important for applications like speech recognition and synthesis systems.
The document discusses principles of universal design and multi-sensory interaction. It covers using multiple senses like sight, sound, and touch to provide richer interaction. Speech and non-speech sounds are explored as input and output methods. Handwriting recognition and gestures are also covered as alternative interaction techniques. The document examines applications and challenges of different technologies for users with varying abilities.
Literature Review On: ”Speech Emotion Recognition Using Deep Neural Network”IRJET Journal
The document discusses speech emotion recognition using deep neural networks. It first provides an overview of SER and the challenges in the field. It then reviews 20 research papers on the topic, finding that most use deep neural network techniques like CNNs and DNNs for model building. The papers evaluated various datasets and algorithms, with accuracy ranging from 84% to 90%. Overall limitations identified included the need for more data, handling of multiple simultaneous emotions, and improving cross-corpus performance. The literature review contributes to knowledge in using machine learning for SER.
This document describes research on detecting emotions from speech in order to drive facial expressions of virtual characters. It discusses using support vector machines trained on a corpus of over 700 utterances expressing neutral, anger, happiness, or sadness emotions captured from movies and plays. The researchers propose evaluating emotions in speech as mixtures of multiple emotions based on their locations in an emotion space, rather than solely classifying utterances into single emotion categories. This allows them to determine the degree and combination of emotions expressed.
This document summarizes a research paper that proposes using prosody (rhythm, stress, intonation) information from user utterances to help a spoken dialogue system determine the user's level of certainty. It describes annotating a travel dialogue corpus for levels of certainty. Acoustic prosody features are extracted from utterances and used to train a classifier, achieving better certainty classification than a non-prosodic model. The paper argues that determining certainty from prosody could help dialogue systems respond more appropriately based on the user's mental state.
1) Verbal communication consists of both speaking and listening. Listening can be sympathetic, where one shares another's feelings, or empathetic, where one acknowledges another's feelings without sharing them.
2) When speaking, one must consider both what to speak through content development and storage systems, and how to speak through guidelines like speed, clarity and expression.
3) Non-verbal communication includes paralanguage like tone of voice, physical appearance like clothing, body movement, proxemics of personal space, touching, eye movement, and smell. These non-verbal forms often communicate complex messages together and with language.
4.14 Verbal and Nonverbal communication.pptxssuser3c427a1
1) Verbal communication consists of both speaking and listening. Effective listening includes sympathetic listening, where one shares another's feelings, and empathetic listening, where one acknowledges another's feelings without sharing them.
2) When speaking, one must consider both the content or "what to speak" as well as the delivery or "how to speak." The content involves brainstorming ideas and choosing a presentation format. The delivery involves factors like speed, clarity, and expression.
3) Non-verbal communication encompasses bodily cues beyond words, including paralanguage, physical appearance, body movement, proxemics, touching, eye movement, and smell. These cues are interpreted differently across cultures.
ACHIEVING SECURITY VIA SPEECH RECOGNITIONijistjournal
Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
The document summarizes research on designing emotional experiences for mobile user interfaces. It describes:
1. Three empirical studies were conducted to identify emotions associated with smartphone use and design principles.
2. Positive and negative emotion sets were developed based on the studies. 30 positive emotions were identified as potential "emotional solutions".
3. 14 "general solutions" or high-level design principles were developed to deliver the emotional solutions. These include principles like "flow" and "superior quality".
4. 23 specific "UX solutions" were developed that embody the general solutions and can be applied to interface design with flexibility.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study on prosody analysis for speech signals across different emotions. It discusses how prosody relates to features like pitch, duration, jitter and shimmer. The study analyzed speech recordings in Odia language uttered with five emotions - anger, love, neutral, sadness and calmness. Acoustic measurements were made on extracted vowels to analyze impact of emotions on parameters like duration, fundamental frequency, jitter and shimmer. The results provide insights into how prosody conveys emotional information in speech.
This document summarizes an invited talk on recognizing emotions in spontaneous speech. It discusses the challenges of emotion recognition in spontaneous speech compared to acted speech, including the subtlety of emotions, data mismatch between training and testing, difficulty building spontaneous speech corpora, and disagreement among annotators. The talk presents two approaches to emotion recognition from audio and focuses on developing a knowledge-based framework to address spontaneous speech. It highlights ongoing work on classifier error corrections and challenges that remain, such as optimal features, acoustic variability, and incorporating speaker attributes.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
This document summarizes a study on analyzing the acoustic feature patterns of emotion expression in Hindi speech. Six Hindi speakers of different ages recorded 20 sample sentences expressing neutral emotion and four types of emotions: anger, happiness, sadness, and surprise. Acoustic parameters including pitch, duration, intensity, and formants were extracted from the emotional speech samples using PRAAT software. The results showed that anger and surprise emotions had higher mean pitch and pitch range compared to neutral. Anger and surprise also had shorter duration patterns and higher intensity compared to happy and sad emotions. Formant patterns also differed between emotions, with anger and surprise having higher first formant frequency and amplitude in higher formants bands compared to neutral.
Предсказание оттока игроков из World of TanksYandex
Одна из наиболее часто возникающих задач в бизнес-аналитике для компаний — это предсказание оттока клиентов. Ведь если заранее знать, что клиент собирается уйти к конкуренту, его можно попытаться остановить. Задача будет рассмотрена на примере прогнозирования оттока игроков из World of Tanks.
Как принять/организовать работу по поисковой оптимизации сайта, Сергей Царик,...Yandex
Лекция Сергея Царика в Школе вебмастеров: «Как принять/организовать работу по поисковой оптимизации сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Основные этапы и методы поисковой оптимизации
Рассмотрим проработку стратегии продвижения, планирование ресурсов на проект, поймем как нужно прорабатывать семантическое ядро для продвижения, разберемся с очередностью всех работ.
Разложим по полочкам основные приемы оптимизации в связке с внутренними и внешними факторами ранжирования поисковых систем, а также в связке с поведенческими факторами и характеристиками. Разберемся с тем, что же должен делать оптимизатор для достижения топа.
Что должно включать в себя ТЗ на поисковую оптимизацию
Разберемся с основными блоками технического задания от оптимизатора, с тем, каким оно должно быть с точки зрения подачи информации и ее глубины.
Сравнение in-house подхода и агентства
Рассмотрим все «за» и «против» оптимизатора в штате компании и вне её.
На основе каких метрик нужно оценивать эффективность оптимизаторской работы
Выделим ключевые показатели эффективности работы оптимизатора, рассмотрим процесс их измерения, динамику, разберемся с возможными «миксами» и их связкой с мотивацией подрядчика.
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BASIC ANALYSIS ON PROSODIC FEATURES IN EMOTIONAL SPEECHIJCSEA Journal
Speech is a rich source of information which gives not only about what a speaker says, but also about what the speaker’s attitude is toward the listener and toward the topic under discussion—as well as the speaker’s own current state of mind. Recently increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. The focus of this research work is to enhance man machine interface by focusing on user’s speech emotion. This paper gives the results of the basic analysis on prosodic features and also compares the prosodic features
of, various types and degrees of emotional expressions in Tamil speech based on the auditory impressions between the two genders of speakers as well as listeners. The speech samples consist of “neutral” speech as well as speech with three types of emotions (“anger”, “joy”, and “sadness”) of three degrees (“light”, “medium”, and “strong”). A listening test is also being conducted using 300 speech samples uttered by students at the ages of 19 -22 the ages of 19-22 years old. The features of prosodic parameters based on the emotional speech classified according to the auditory impressions of the subjects are analyzed. Analysis results suggest that prosodic features that identify their emotions and degrees are not only speakers’ gender dependent, but also listeners’ gender dependent.
Annotation Of Emotion Carriers In Personal NarrativesErica Thompson
This document describes research on annotating emotion carriers in personal narratives. It defines emotion carriers as speech or text segments that best explain the emotional state of the narrator. The researchers experimented with manually annotating emotion carriers in German personal narratives from the Ulm State-of-Mind in Speech corpus. They believe this annotated resource could be used to automatically extract emotion carriers from personal narratives, advancing understanding of narratives. The task is challenging as the same term may or may not carry emotion depending on context, making the search space for identifying carriers large.
1) Affective computing aims to expand human emotional intelligence to machines by creating socially intelligent machines that can respond appropriately according to the situation and interlocutor.
2) There are two main approaches to modeling emotions in affective computing: discrete theories that identify basic emotions like Ekman's six emotions, and continuous theories that describe emotions along dimensions of arousal and valence.
3) Empath's goal is to recognize emotions from speech regardless of language, which presents challenges of combining speech processing with emotion recognition from voice cues alone. Empath is developing methods to extract pitch, intensity, and speech rate from voice samples to train models to classify emotions.
Abstract: Speech technology and systems in human computer interaction have witnessed a stable and remarkable advancement over the last two decades. Today, speech technologies are commercially available for an unlimited but interesting range of tasks. These technologies enable machines to respond correctly and reliably to human voices, and provide useful and valuable services. This thesis presents the characteristics of emotion in voice and on that basis propose a new method to detecting emotion in a simplified way by using a prosodic features and spectral from speech. We classify seven emotions: happy, anger, fear, disgust, sadness and neutral inner state. This thesis discusses the method to extract features from a recorded speech sample, and using those features, to detect the emotion of the subject. Every emotion comprises different vocal parameters exhibiting diverse characteristics of speech, which is used for preliminary classification. Then Mel-Frequency Cepstrum Coefficient (MFCC) method was used to extract spectral features. The MFCC coefficients were again trained by Artificial Neural Network (ANN) which then classifies the input in particular emotional class.
Facial Expression Recognition System: A Digital Printing Applicationijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Facial Expression Recognition System: A Digital Printing Applicationijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document summarizes a study on the impact of emotion on prosody analysis in speech. The study analyzed speech samples recorded from actors expressing different emotions like love, anger, calm, sadness and neutral. It measured acoustic parameters like vowel duration, fundamental frequency, jitter and shimmer for the different emotions. The results showed that speech expressing love had longer vowel durations, while sad speech had longer durations for certain vowels. This indicates emotion impacts prosodic features of speech, which is important for applications like speech recognition and synthesis systems.
The document discusses principles of universal design and multi-sensory interaction. It covers using multiple senses like sight, sound, and touch to provide richer interaction. Speech and non-speech sounds are explored as input and output methods. Handwriting recognition and gestures are also covered as alternative interaction techniques. The document examines applications and challenges of different technologies for users with varying abilities.
Literature Review On: ”Speech Emotion Recognition Using Deep Neural Network”IRJET Journal
The document discusses speech emotion recognition using deep neural networks. It first provides an overview of SER and the challenges in the field. It then reviews 20 research papers on the topic, finding that most use deep neural network techniques like CNNs and DNNs for model building. The papers evaluated various datasets and algorithms, with accuracy ranging from 84% to 90%. Overall limitations identified included the need for more data, handling of multiple simultaneous emotions, and improving cross-corpus performance. The literature review contributes to knowledge in using machine learning for SER.
This document describes research on detecting emotions from speech in order to drive facial expressions of virtual characters. It discusses using support vector machines trained on a corpus of over 700 utterances expressing neutral, anger, happiness, or sadness emotions captured from movies and plays. The researchers propose evaluating emotions in speech as mixtures of multiple emotions based on their locations in an emotion space, rather than solely classifying utterances into single emotion categories. This allows them to determine the degree and combination of emotions expressed.
This document summarizes a research paper that proposes using prosody (rhythm, stress, intonation) information from user utterances to help a spoken dialogue system determine the user's level of certainty. It describes annotating a travel dialogue corpus for levels of certainty. Acoustic prosody features are extracted from utterances and used to train a classifier, achieving better certainty classification than a non-prosodic model. The paper argues that determining certainty from prosody could help dialogue systems respond more appropriately based on the user's mental state.
1) Verbal communication consists of both speaking and listening. Listening can be sympathetic, where one shares another's feelings, or empathetic, where one acknowledges another's feelings without sharing them.
2) When speaking, one must consider both what to speak through content development and storage systems, and how to speak through guidelines like speed, clarity and expression.
3) Non-verbal communication includes paralanguage like tone of voice, physical appearance like clothing, body movement, proxemics of personal space, touching, eye movement, and smell. These non-verbal forms often communicate complex messages together and with language.
4.14 Verbal and Nonverbal communication.pptxssuser3c427a1
1) Verbal communication consists of both speaking and listening. Effective listening includes sympathetic listening, where one shares another's feelings, and empathetic listening, where one acknowledges another's feelings without sharing them.
2) When speaking, one must consider both the content or "what to speak" as well as the delivery or "how to speak." The content involves brainstorming ideas and choosing a presentation format. The delivery involves factors like speed, clarity, and expression.
3) Non-verbal communication encompasses bodily cues beyond words, including paralanguage, physical appearance, body movement, proxemics, touching, eye movement, and smell. These cues are interpreted differently across cultures.
ACHIEVING SECURITY VIA SPEECH RECOGNITIONijistjournal
Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
The document summarizes research on designing emotional experiences for mobile user interfaces. It describes:
1. Three empirical studies were conducted to identify emotions associated with smartphone use and design principles.
2. Positive and negative emotion sets were developed based on the studies. 30 positive emotions were identified as potential "emotional solutions".
3. 14 "general solutions" or high-level design principles were developed to deliver the emotional solutions. These include principles like "flow" and "superior quality".
4. 23 specific "UX solutions" were developed that embody the general solutions and can be applied to interface design with flexibility.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study on prosody analysis for speech signals across different emotions. It discusses how prosody relates to features like pitch, duration, jitter and shimmer. The study analyzed speech recordings in Odia language uttered with five emotions - anger, love, neutral, sadness and calmness. Acoustic measurements were made on extracted vowels to analyze impact of emotions on parameters like duration, fundamental frequency, jitter and shimmer. The results provide insights into how prosody conveys emotional information in speech.
This document summarizes an invited talk on recognizing emotions in spontaneous speech. It discusses the challenges of emotion recognition in spontaneous speech compared to acted speech, including the subtlety of emotions, data mismatch between training and testing, difficulty building spontaneous speech corpora, and disagreement among annotators. The talk presents two approaches to emotion recognition from audio and focuses on developing a knowledge-based framework to address spontaneous speech. It highlights ongoing work on classifier error corrections and challenges that remain, such as optimal features, acoustic variability, and incorporating speaker attributes.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
This document summarizes a study on analyzing the acoustic feature patterns of emotion expression in Hindi speech. Six Hindi speakers of different ages recorded 20 sample sentences expressing neutral emotion and four types of emotions: anger, happiness, sadness, and surprise. Acoustic parameters including pitch, duration, intensity, and formants were extracted from the emotional speech samples using PRAAT software. The results showed that anger and surprise emotions had higher mean pitch and pitch range compared to neutral. Anger and surprise also had shorter duration patterns and higher intensity compared to happy and sad emotions. Formant patterns also differed between emotions, with anger and surprise having higher first formant frequency and amplitude in higher formants bands compared to neutral.
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Предсказание оттока игроков из World of TanksYandex
Одна из наиболее часто возникающих задач в бизнес-аналитике для компаний — это предсказание оттока клиентов. Ведь если заранее знать, что клиент собирается уйти к конкуренту, его можно попытаться остановить. Задача будет рассмотрена на примере прогнозирования оттока игроков из World of Tanks.
Как принять/организовать работу по поисковой оптимизации сайта, Сергей Царик,...Yandex
Лекция Сергея Царика в Школе вебмастеров: «Как принять/организовать работу по поисковой оптимизации сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Основные этапы и методы поисковой оптимизации
Рассмотрим проработку стратегии продвижения, планирование ресурсов на проект, поймем как нужно прорабатывать семантическое ядро для продвижения, разберемся с очередностью всех работ.
Разложим по полочкам основные приемы оптимизации в связке с внутренними и внешними факторами ранжирования поисковых систем, а также в связке с поведенческими факторами и характеристиками. Разберемся с тем, что же должен делать оптимизатор для достижения топа.
Что должно включать в себя ТЗ на поисковую оптимизацию
Разберемся с основными блоками технического задания от оптимизатора, с тем, каким оно должно быть с точки зрения подачи информации и ее глубины.
Сравнение in-house подхода и агентства
Рассмотрим все «за» и «против» оптимизатора в штате компании и вне её.
На основе каких метрик нужно оценивать эффективность оптимизаторской работы
Выделим ключевые показатели эффективности работы оптимизатора, рассмотрим процесс их измерения, динамику, разберемся с возможными «миксами» и их связкой с мотивацией подрядчика.
Структурированные данные, Юлия Тихоход, лекция в Школе вебмастеров ЯндексаYandex
Лекция Юлия Тихоход в Школе вебмастеров: «Структурированные данные на поиске»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что такое микроразметка и в чём её польза
Что такое микроразметка (семантическая разметка, семантическая микроразметка) и кому она нужна. Очень кратко — всё, что я знаю о применении семантической разметки поисковыми системами и другими веб-сервисами.
Передача данных в машиночитаемом виде
Какие ещё есть способы передать данные о сайте поисковым системам кроме микроразметки, особенности разных способов. Что бывает с плохими вебмастерами, которые пытаются обмануть поисковые системы и передать неверные данные.
Типы разметки
Из чего состоит микроразметка, какие бывают словари и синтаксисы. Популярные сочетания словарей и синтаксисов, как правильно выбирать нужную комбинацию для своего сайта.
Передача данных об интернет-магазине
Разбор семантической разметки: что в принципе доступно для разметки в интернет-магазине, что это даёт, а что можно не размечать вовсе.
Проверка правильности микроразаметки
Ошибки в микроразметке, способы их обнаружения и исправления. Популярные валидаторы микроразметки. Какие ошибки непременно нужно исправлять, а что можно игнорировать.
Представление сайта в поиске, Сергей Лысенко, лекция в Школе вебмастеров ЯндексаYandex
Лекция Сергея Лысенко в Школе вебмастеров: «Представление сайта в поиске»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Основные элементы сниппетов: как влиять на их формирование
Как по внешнему виду и содержанию визитки судят, стоит ли «связываться», так и по представлению сайта на странице выдачи пользователи решают, стоит ли переходить на сайт. Как изменить представление сайта в выдаче поисковых систем? Что может повлиять на CTR и что для этого нужно сделать? Рассмотрим фавиконки, навигационные цепочки, быстрые ссылки и многое, многое другое.
Зачем нам заголовок: как им управлять
Что должно быть в заголовке, а чего уж точно не стоит делать. Как избавиться от мусора и расставить акценты. И как это скажется на представлении сайта в поиске.
Основной контент аннотации и мета-описания: что нам они дают
Сниппет — зачем он нужен? Как обрабатываются данные для аннотаций? Что в сниппете помогает, а что мешает пользователю сориентироваться? Как подсказать поисковой системе, что выводить в сниппете? От Open Graf до schema.org. Инструменты, возможности, рекомендации.
Плохие методы продвижения сайта, Екатерины Гладких, лекция в Школе вебмастеро...Yandex
Лекция Екатерины Гладких в Школе вебмастеров: «Плохие методы продвижения сайта»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как завязывают с портянками
Как использовать wordstat, чтобы превратить текст в SEO-портянку. Как Яндекс определяет текстовый спам и какие ограничения могут быть применены к сайтам, злоупотребляющим ключевыми словами.
Эффектное размещение SEO-ссылок
Какие бывают SEO-ссылки и как они классифицируются в базе Яндекса. В чём отличие SEO-ссылок от рекламы. Как размещать SEO-ссылки наиболее эффектно. Методы борьбы против ссылочного спама – АГС и Минусинск. Снятие ссылок.
Поведенческие факторы, медитативные практики
Популярные сервисы накрутки: как это работает и как это не работает. Методы накрутки и методы борьбы с мошенничеством. Примеры пользовательских сессий и кто на самом деле посещает ваш сайт. Как выйти из-под санкций за накрутку поведенческих факторов.
Основные принципы ранжирования, Сергей Царик и Антон Роменский, лекция в Школ...Yandex
Лекция Сергея Царика и Антона Роменского в Школе вебмастеров: «Основные принципы ранжирования»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как работает поиск
При запросе пользователя к поисковой системе происходит множество процессов, которые позволяют дать наиболее релевантный ответ. Рассмотрим основные механизмы формирования выдачи: формулы, Матрикснет, персонализацию и обновления.
Что учитывается при ранжировании сайтов
Так как сайты разные и по-разному решают пользовательские задачи, при ранжировании поисковой системе нужно учитывать множество факторов. Поговорим о том, что обязательно должно быть на сайте для правильной индексации.
Ещё о факторах ранжирования
Какой контент действительно важен и как его правильно представить. Для правильного ранжирования сайта важно разобраться с его региональной привязкой. Разберёмся, какой регион присваивать сайту и как сделать это правильно.
Реальный кейс долгосрочной работы над позициями
Посмотрим на реальном примере, как изменялись основные жизненные характеристики (трафик, конверсии) сайта на пути в топ выдачи поисковых систем.
Основные принципы индексирования сайта, Александр Смирнов, лекция в Школе веб...Yandex
Лекция Александра Смирнова в Школе вебмастеров: «Основные принципы индексирования сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Как поиск находит страницу, её путь до появления в поиске
Поисковые системы постоянно собирают информацию о страницах в интернете. Как же это происходит и как добавить страницы своего сайта в поиск? Проверка индексирования сайта.
Как управлять роботом (зеркала, sitemap, robots.txt)
Множество сайтов в интернете доступны сразу по нескольким адресам. Как указать поисковому роботу на основной и как скорректировать индексирование?
Особенности индексирования
Современные сайты используют различные технологии в своей работе. Рассмотрим, как настроить их правильно и сделать контент доступным для робота.
Как улучшить индексирование (дубли, HTTP-ответ, удаление из поиска)
В поиск попадают различные страницы, которые известны роботу. Какие нужны, а какие нет? Как повлиять на их индексирование?
Мобильное приложение: как и зачем, Александр Лукин, лекция в Школе вебмастеро...Yandex
Лекция Александра Лукина в Школе вебмастеров: «Мобильное приложение: как и зачем»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Проектирование. Быть или не быть
Обсудим обоснование для разработки мобильного приложения — какую ценность оно может принести для проекта и бизнеса. Определим основные типы приложений и сценарии использования. Рассмотрим основные технологии и выбор оптимальных для конкретных задач. ТЗ — как оценить и какие особенности необходимо учесть.
Разработка. Важные детали
На что обратить внимание на этапе разработки и тестирования, заметки по специфике мобильных экосистем. Выбираем арсенал SDK для всестороннего анализа проекта в полёте.
Публикация и продвижение
Кратко рассмотрим специфику Google Play и AppStore. Проведём экскурс в мир мобильного маркетинга, подчеркнём сходства с вебом и отличия от него. Рассмотрим ключевые метрики для анализа продукта и процесса продвижения, а также способы их повышения.
Сайты на мобильных устройствах, Олег Ножичкин, лекция в Школе вебмастеров Янд...Yandex
Лекция Олега Ножичкина в Школе вебмастеров: «Сайты на мобильных устройствах»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Статистика и тренды по мобильному интернету
Основные показатели мобильного интернет-рынка. Тенденции роста мобильной аудитории.
Новые алгоритмы ранжирования поисковых систем
Адаптация сайта к мобильным пользователям и её влияние на позиции в поисковой выдаче.
Возможности для бизнеса в мобильном вебе
Мобильный сайт позволяет воспользоваться дополнительными возможностями взаимодействия с пользователем. Рассмотрим конкретные примеры.
Мобильный сайт и приложение — в чём разница
Чем отличается мобильное приложение от мобильно сайта. Какие преимущества и недостатки у каждого варианта.
Представление сайтов на мобильных устройствах
Адаптивные сайты. Мобильные сайты. Сайты для десктопа. Чем они отличаются, какие преимущества у каждого типа и нужно ли переключаться между мобильной и десктоп-версиями?
Удобный мобильный сайт для пользователя
Поведение пользователей на мобильном сайте. Отличия от десктопа, достижение целей и простые правила увеличения конверсии.
Специфика разработки мобильного сайта
Особенности проектирования, разработки и тестирования сайтов.
Инструменты для разработки мобильных сайтов
Готовые инструменты для проектирования и тестирования. Примеры фреймворков.
Качественная аналитика сайта, Юрий Батиевский, лекция в Школе вебмастеров Янд...Yandex
Лекция Юрия Батиевского в Школе вебмастеров: «Качественная аналитика сайта»
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что мы хотим от аналитики сайта
На какие вопросы должна отвечать аналитика сайта. Как аналитика сайта связана с аналитикой бизнеса. На какие блоки можно поделить аналитику онлайн-процессов. Какой должна быть идеальная аналитическая система.
Анализ общих показателей бизнеса
Как построить систему аналитики бизнеса в интернете. Ключевые показатели эффективности (KPI). Построение воронки продаж. Business Intelligence — сквозная аналитика всех процессов.
Обзор инструментов для анализа сайта и аудитории
Яндекс.Метрика и Google Analytics как основа веб-аналитики. Инструменты для веб-мастеров. Инструменты для анализа действий пользователей (Kiss-metrics, Woopra, Mixpanel). Системы для подсчета целевых действий, CPA и ROMI.
Анализ каналов привлечения клиентов
Как анализировать источники трафика. Популярные инструменты для анализа.
Пройти тест по теме
Процесс развертывания системы аналитики сайта
Подготовка к установке систем веб-аналитики. Тонкости установки и настройки трекеров. Подключение коллтрекинга и дополнительных инструментов фиксации целевых действий. Настройка пользовательских сценариев. Пример по анализу пользовательского сценария.
Что можно и что нужно измерять на сайте, Петр Аброськин, лекция в Школе вебма...Yandex
Лекция Петра Аброськина в Школе вебмастеров: «Что можно и что нужно измерять на сайте».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Базовые принципы веб-аналитики
Как работает веб-аналитика и какие подводные камни есть в учёте и анализе данных. Как правильно работать с данными.
Основные метрики и термины
Посетители, визиты, глубина просмотра, время на сайте — какие метрики важны и чем они отличаются.
Как выбрать правильный KPI
Самый важный этап в веб-аналитике и продвижении сайта. Какие цели выбрать интернет-магазину, сайту услуг, контентному проекту и т.д.
Ключевые группы отчетов и применение знаний на практике
Семь главных типов отчётов для бизнеса. Анализ контекстной рекламы, SEO и контента сайта — на конкретных примерах.
Как правильно поставить ТЗ на создание сайта, Алексей Бородкин, лекция в Школ...Yandex
Лекция Алексея Бородкина в Школе вебмастеров: «Как правильно поставить ТЗ на создание сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
ТЗ: две буквы с большим потенциалом
Что такое техническое задание. Какое место оно занимает в веб-разработке. Какие цели преследует. И каким требованиям оно должно отвечать.
Что нужно сделать, прежде чем садиться за ТЗ
Зачем нужна подготовка к написанию ТЗ. Какую информацию нужно собрать и как выстроить этот процесс. На каком этапе веб-разработки нужно писать ТЗ — и что будет, если этот момент упустить. Какое отношение имеют к ТЗ прототипы, пользовательские истории и прочие инструменты проектирования.
Хорошее ТЗ
Как соединить в один документ описание интерфейсов, структуру данных и много чего ещё. Структура правильного, хорошего ТЗ с подробным разбором каждого пункта. С какой стороны приступать и как эффективнее всего выстроить работу.
Кто должен писать ТЗ
Кто может написать хорошее ТЗ. Где найти такого человека и как встроить его в общие процессы. Что делать, если ТЗ пишет сам заказчик.
Плохое ТЗ
Популярные ошибки. Чем они ужасны и как их избежать.
Жизнь с ТЗ
По какой схеме нужно согласовывать ТЗ. Как применять его в дальнейшей работе. Кому не нужно показывать ТЗ ни при каких обстоятельствах. Что делать, если ТЗ никому не нравится.
ТЗ по ГОСТ: ад на Земле
Краткая история развития ТЗ со времён Брежнева и до наших дней. Почему я старательно избегаю слова «ТЗ». Почему вы должны нервно вздрагивать при слове «ГОСТ». Что делать, если вы работаете с госзаказчиком.
Как защитить свой сайт, Пётр Волков, лекция в Школе вебмастеровYandex
Лекция Петра Волкова в Школе вебмастеров: «Как защитить свой сайт».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Актуальные типы угроз и динамика их развития
Компрометация сервера и её последствия. Распределённые атаки типа «отказ в обслуживании». Подмена или добавление рекламы на стороне клиента. Атаки, направленные на пользователей. Проблемы, связанные со внешним содержимым.
Управление рисками безопасности веб-сайтов
Разные типы сайтов подвержены разным типам рисков информационной безопасности. Понимание целей и подходов злоумылшенников как ключ к эффективному снижению рисков. Методы монетизации атак на сайты.
Доступный инструментарий и методики для обеспечения безопасности
Открытые инструменты форензики для типовых и сложных проектов. Системы обнаружения вторжений, подходы к проектированию безопасности в архитектуре и процессах.
Как правильно составить структуру сайта, Дмитрий Сатин, лекция в Школе вебмас...Yandex
Лекция Дмитрия Сатина в Школе вебмастеров: «Как правильно составить структуру сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Структура сайта, ориентированная на человека; построение структуры, карточная сортировка
Содержимое сайтов часто организовано так, как кажется удобным разработчику или контент-менеджеру компании. Чаще всего такие структуры неудобны для реальных посетителей, потому что не совпадают с их знаниями, не поясняют, как устроен материал, и не помогают найти желаемое. Структура, ориентированная на пользователя, повышает вероятность того, что посетители найдут нужную информацию или товар и сделают это быстро.
Стройте структуру, исходя из пользовательских сценариев. Выделение на сайте разделов, соответствующих структуре компании или схеме процесса закупки, как правило, усложняет навигацию для пользователя. Правильная структура учитывает уровень знаний покупателя и использует понятные ему термины и способы группировки.
Разные типы структур, средства навигации, дальнейший поиск информации на странице
Структуры сайтов, на которых ищут что-то определённое, отличаются от тех, что используются на сайтах, посетители которых ещё не уверены, что именно они хотят или как называется нужная вещь. Строгие структуры — например, организация по наименованию товара, производителю, — предполагают один способ группировки. При нестрогой организации данные можно группировать по теме, по жизненной ситуации и так далее. Используйте средства навигации, которые помогают понять, как организован материал. Решая, какой будет визуальная реализация навигации на сайте, необходимо учитывать количество разделов и связи �
Технические особенности создания сайта, Дмитрий Васильева, лекция в Школе веб...Yandex
Лекция Дмитрия Васильева в Школе вебмастеров: «Технические особенности создания сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Сайт — расплывчатое понятие
Раньше под словом «сайт» понимался набор HTML-страниц, расположенных в домене второго или третьего уровня. Появление социальных сетей размыло это понятие.
Как выбрать домен
Различные варианты, и какой из них подойдёт именно вашему сайту: доменные зоны, читаемые и нечитаемые домены, кириллица и латиница.
Подходы к созданию сайтов
Первые сайты делались на чистом HTML. Сейчас такой способ ещё встречается, но подавляющее большинство веб-страниц создаются при помощи CMS, фреймворков, конструкторов.
Составные сущности: структура, макеты дизайна, интерактивные элементы, контент, система прав. Размещение сайта на хостинге. Российские и зарубежные, дорогие и дешевые, облачные и традиционные провайдеры. Кратко о тонкостях взаимодействия с ними.
Что такое HTTPS
Всё более популярный безопасный протокол доступа к сайту. Нужен ли он вам и в каких случаях. Как выбрать платформу для сайта, основные системы управления сайтом (CMS) и конструкторы.
Сайт после запуска
Сайты создаются с конкретной целью, обычно связанной с получением дохода. Как контент сайта и его технические характеристики напрямую могут влиять на бизнес-эффективность.
Конструкторы для отдельных элементов сайта, Елена Першина, лекция в Школе веб...Yandex
Лекция Елены Першиной в Школе вебмастеров: «Конструкторы для отдельных элементов сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
О пользе тех или иных технологий
Взгляд в будущее, короткий обзор других полезных технологий и «опасностей», которые подстерегают на пути к правильному их выбору.
Как выбрать поиск для сайта
Поиск для сайта — важный инструмент навигации. Чтобы оценить качество поиска по своему сайту, посмотрите на количество уходов со страницы результатов. Полнота, скорость индексирования, обработка запросов (исправление ошибок, опечаток, неправильной раскладки) — без этого невозможно представить качественный поиск.
Как выбрать карты для сайта
Уход посетителя с сайта на «большие» Яндекс.Карты за точной информацией об организации может обернуться потерей клиента, который уже был готов к покупке. Чтобы этого не допустить, лучше сделать интерактивную карту прямо на сайте.
Автоматизация оплаты на сайте
Люди привыкают платить картой, сегодня даже уличные киоски принимают их. Поэтому многим посетителям кажется «подозрительным» интернет-магазин, в котором недоступны электронные платежи. Начать приём банковских карт в онлайне очень просто, главное выбрать для этого подходящую технологию.
Перевод важных страниц
На каких языках говорит ваша аудитория, много ли у вас посетителей из-за рубежа? Ответы на эти вопросы даст Яндекс.Метрика. Именно она поможет оценить, нужно ли тратиться на профессионального переводчика и готовить отдельные описания товаров или новости на других языках. Во многих случаях для совершения покупки достаточно и простого машинного перевода. Узнайте, как его настроить, чтобы ключевые разделы сайта автоматически переводились для иностранных посетителей.
Социальная интеграция
Как заставить пользователей говорить о себе в социальных сетях? В первую очередь нужно сделать хороший продукт или услугу, но и без удобного инструмента для «шаринга» в соцсетях — никуда. Рекомендации о том, как выбрать и установить такой инструмент к себе на сайт.
Контент для интернет-магазинов, Катерина Ерошина, лекция в Школе вебмастеров ...Yandex
Лекция Катерины Ерошиной в Школе вебмастеров: «Контент для интернет-магазинов».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Виды контента для интернет-магазинов
Основные страницы, карточки товаров, каталог в целом. Письма покупателям. Статьи для интернет-магазина.
Основные сервисные страницы: что нужно знать покупателю
О страницах доставки, оплаты, контактов, условий работы.
Страница товара интернет-магазина: какой нужен текст, чтобы товар нашли
Признаки товаров. Сниппеты товарных позиций. Когда текст не нужен вообще. Постоянная и техническая информация на карточке.
Блог и внешние публикации интернет-магазина
О чем писать, чтобы подогреть интерес к магазину. Сторителлинг. UGC: методы вовлечения (кратко).
Персонализация интернет-магазина: стать ближе к покупателю
Красивый пример личного бренда директора магазина.
Копирайтинг для интернет-магазина: на чём можно и нельзя экономить
Что делать, если у вас 100 000 товарных позиций и они постоянно меняются.
Хорошее ТЗ копирайтеру для наполнения интернет-магазина
Что должен знать копирайтер, чтобы не писать ерунду.
Как оценить работу копирайтера
Стандартные проверки. Контроль качества текста средствами аналитики.
Как написать хороший текст для сайта, Катерина Ерошина, лекция в Школе вебмас...Yandex
Лекция Катерины Ерошиной в Школе вебмастеров: «Как написать хороший текст для сайта».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Назначение и типы текстов на сайте и вне его
Цель текста — влиять на поведение пользователя. Самое простое — информировать, самое сложное — привести к покупке. Виды текстов для внешних публикаций. Белые книги и другие способы подтвердить экспертизу.
Контент-план для наполнения, развития сайта и внешних публикаций
Как проектировать контент для нового сайта. Как наращивать информационную массу сайта. Внешние контакты с потребителем.
Разные уровни вовлечения: информируем, продаём, помогаем
Пройти по пути покупателя, выдавать информацию, необходимую для совершения следующего шага. Ловушки на этом пути.
Информационный стиль: применение с пониманием
Чистить текст без фанатизма. Эмоциональное вовлечение. Рациональное обоснование.
Структура и вёрстка
Заголовки и подзаголовки, списки, абзацы, иерархия подачи информации.
SEO-аспекты и LSI-копирайтинг
Понимание ценности ключей. Зачем копирайтеру нужно семантическое ядро.
Оценка качества текста (чеклист)
Уникальность, фактическая достоверность, соответствие целям, информационная плотность, грамотность.
Usability и дизайн - как не помешать пользователю, Алексей Иванов, лекция в Ш...Yandex
Лекция Алексея Иванова в Школе вебмастеров: «Usability и дизайн: как не помешать пользователю».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Что такое юзабилити и почему оно важно
Поведение пользователей на сайте и достижение ими запланированных целей зависит не только от контента, но и от удобства сайта.
Информационное и функциональное наполнение сайта
Перед созданием сайта нужно правильно определить, какая информация и какой функционал должны быть на сайте. При этом нужно исходить не из того, что у вас есть, а из того, что будет нужно будущим посетителям вашего сайта.
Проектирование входных страниц
В зависимости от целей сайта и источников посетителей нужно сформулировать требования к входным страницам сайта и их содержанию.
Сценарии поведения пользователя
Для правильного распределения информации нужно описать сценарии взаимодействия с сайтом для разных групп посетителей. Рассмотрим методы совмещения разных сценариев на одном сайте.
Пройти тест по теме
Управление конверсией
В большинстве случаев мы ждем от посетителя сайта какого-то целевого действия. Это может быть регистрация, отправка заявки, звонок или что-то ещё. Вы увидите способы мотивации посетителей к совершению целевого действия для различных типов сайтов.
Пройти тест по теме
Основные принципы распределения информации
В рамках этого блока вы увидите, как нужно распределять информацию на странице, чтобы посетители увидели всё, что вы хотите им показать.
Мобильная версия сайта и принципы юзабилити
Всё больше посетителей приходят на сайт с мобильных устройств. Рассмотрим основные особенности взаимодействия с информацией с мобильного устройства и подходы к адаптации сайта под них.
Cайт. Зачем он и каким должен быть, Алексей Иванов, лекция в Школе вебмастеро...Yandex
Лекция Алексея Иванова в Школе вебмастеров Яндекса: «Сайт. Зачем он и каким должен быть».
https://academy.yandex.ru/events/webmasters_school/yawebm2015/
Типы сайтов и потребности аудитории
В зависимости от решаемых задач, сайты можно разделить на несколько характерных типов с разными функциями и контентом. Перед созданием сайта важно понять, чего ждут посетители и какими хотят видеть веб-страницы. При этом на один и тот же сайт может попадать разная аудитория, которая ведёт себя по-разному и каждая имеет свои потребности. Для каждого сегмента нужно разработать отдельные сценарии взаимодействия с информацией на вашей площадке.
Сайт с точки зрения бизнеса
Чаще всего сайт создается для решения конкретных бизнес-задач. Рассмотрим различные типы монетизации сайтов и особенности каждого из них.
Основные показатели и методы измерения
Одно из главных преимуществ цифровых каналов — детальная аналитика взаимодействия посетителей с сайтом. В данном блоке рассмотрим основные инструменты измерения, ключевые показатели сайта, на которые нужно обращать внимание, и подходы к интерпретации полученных данных для принятия решений.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Automatic Recognition of Emotions in Speech: Models and Methods. Аndreas wendemuth
1. 130.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Automatic Recognition of Emotions in Speech:
models and methods
Prof. Dr. Andreas Wendemuth
Univ. Magdeburg, Germany
Chair of Cognitive Systems
Institute for Information Technology and Communications
YAC / Yandex, 30. October 2014, Moscow
2. 230.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Recorded speech starts as an acoustic signal. For decades, appropriate
methods in acoustic speech recognition and natural language processing
have been developed which aimed at the detection of the verbal content of
that signal, and its usage for dictation, command purposes, and assistive
systems. These techniques have matured to date. As it shows, they can be
utilized in a modified form to detect and analyse further affective
information which is transported by the acoustic signal: emotional content,
intentions, and involvement in a situation. Whereas words and phonemes are
the unique symbolic classes for assigning the verbal content, finding
appropriate descriptors for affective information is much more difficult.
We describe the corresponding technical steps for software-supported affect
annotation and for automatic emotion recognition, and we report on the
data material used for evaluation of these methods.
Further, we show possible applications in companion systems and in dialog
control.
Abstract
3. 330.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
4. 430.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Affective Factors in Man-Machine-Interaction
5. 530.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Affective Terms - Disambiguation
Emotion [Becker 2001]
• short-time affect
• bound to specific events
Mood [Morris 1989]
medium-term affect•
• not bound to specific events
Personality [Mehrabian 1996]
• long-term stable
• represents individual characteristics
6. 630.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Emotion: the PAD-space
• Dimensions:
• pleasure / valence (p),
• arousal (a) and
• dominance (d)
• values each from -1.0 bis 1.0
• “neutral” at center
• defines octands, e.g. (+p+a+d)
Siegert et al. 2012 Cognitive Behavioural Systems. COST
7. 730.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Correlation of emotion and mood
In order to make it measurabble, there has to be an empirical correlation of
moods to PAD space (emotion octands). [Mehrabian 1996]
Moods for octands in PAD space
PAD mood PAD mood
+++ Exuberant
++- Dependent
+-+ Relaxed
+- - Docile
- - - Bored
- -+ Disdainful
-+- Anxious
-++ Hostile
Siegert et al. 2012 Cognitive Behavioural Systems. COST
8. 830.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Personality and PAD-space
Unique personality model: Big Five [Allport and Odbert 1936]
5 strong independent factors
[Costa and McCrae 1985] presented the five-factor personality
inventory
deliberately applicable to non-clinical environments
•
•
•
•
Neuroticism
Extraversion
openness
agreeableness
conscientiousness
•
•
•
•
•
• measurable by questionnaires (NEO FFI test)
• Mehrabian showed a relation between the Big Five Factors (from Neo-FFI,
scaled to [0,1]) and PAD-space. E.g.:
• P := 0.21 · extraversion +0.59 · agreeableness +0.19 · neuroticism
(other formulae available for arousal and dominance)
Siegert et al. 2012 Cognitive Behavioural Systems. COST
9. 930.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
10. 1030.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Speech (Semantics)
• Non-semantic utterances („hmm“, „aehhh“)
• Nonverbals (laughing, coughing, swallowing,…)
• Emotions in speech
Interaction modalities –
what a person „tells“
11. 1130.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Discourse Particles
Especially the intonation reveals details about the speakers attitude but
is influenced by semantic and grammatical information.
investigate discourse particles (DPs)
• can’t be inflected but emphasized
• occurring at crucial communicative points
• have specific intonation curves (pitch-contours)
• thus may indicate specific functional meanings
Siegert et al. 2013 WIRN Vietri
12. 1230.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
The Role of Discourse Particles for Human Interaction
J. E. Schmidt [2001] presented an empirical study where he could
determine seven form-function relations of the DP “hm”:
Siegert et al. 2013 WIRN Vietri
Name idealised
pitch-contour
Description
DP-A attention
DP-T thinking
DP-F finalisation signal
DP-C confirmation
DP-D decline∗
DP-P positive
assessment
DP-R request to respond
13. 1330.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
The Role of Discourse Particles for Human Interaction
• [Kehrein and Rabanus, 2001] examined different conversational styles
and confirmed the form-function relation.
• [Benus et al., 2007] investigated the occurrence frequency of specific
backchannel words for American English HHI.
• [Fischer et al., 1996]: the number of partner-oriented signals is
decreasing while the number of signals indicating a task-oriented or
expressive function is increasing
• Research Questions
• Are DPs occurring within HCI?
• Which meanings can be determined?
• Which form-types are occurring?
Siegert et al. 2013 WIRN Vietri
14. 1430.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Speech (Semantics)
• Non-semantic utterances („hmm“, „aehhh“)
• Nonverbals (laughing, coughing, swallowing,…)
• Emotions in speech
• Eye contact / direction of sight
• General Mimics
• Face expressions (Laughing, angryness,..)
• Hand gesture, arm gesture
• Head posure, body posure
• Bio-signals (blushing, paleness, shivering, frowning…)
• Pupil width
• Haptics: Direct operation of devices (keyboard, mouse, touch)
• Handwriting, drawing, sculpturing, …
Interaction modalities – what a person „tells“ with other modalities
15. 1530.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Indirect expression (pauses, idleness, fatigueness)
• Indirect content (humor, irony, sarcasm)
• Indirect intention (hesitation, fillers, discourse particles)
What speech can (indirectly) reveal
16. 1630.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Recognizing speech, mimics, gestures, poses, haptics, bio-signals: indirect information
• Many (most) modalities need data-driven recognition engines
• Unclear categories (across modalities?)
• Robustness of recognition in varying / mobile environments
Technical difficulties
17. 1730.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
So, really, you have raw data.
Now you (hopefully) have recorded (multimodal)
data with (reliable) emotional content
but what does it convey?
Actually, you have a (speech) signal,
18. 1830.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
19. 1930.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
transcriptions (intended things which happened)
(Speech: „Nice to see you“; Mimics: „eyes open, lip corners up“; … )
Now you need:
and
annotations (unintended events, or the way how it happened).
Speech: heavy breathing, fast, happy; Mimics: smile, happiness; …
Both processes require
labelling: tagging each recording chunk with marks, which
correspond to the relevant transcription / annotation categories
20. 2030.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Trained transcribers / annotators with high intra- and interpersonal reliability (kappa
measures)
• Time aligned (synchronicity!), simultaneous presentation of all modalities to the transcriber /
annotator
• Selection of (known) categories for the transcriber / annotator
• Labelling
How to transcribe / annotate?
21. 2130.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Clear (?) modal units of investigation / categories e.g.:
• Speech: phonemes, syllables, words
• Language: letters, syllables, words
• Request: content! (orgin city, destination city, day, time)
• Dialogues: turn, speaker, topic
• Situation Involvement: object/subject of attention, diectics, active/passive participant
• Mimics: FACS (Facial Action Coding System) -> 40 action units
• Big 5 Personality Traits (OCEAN)
• Sleepiness (Karolinska Scale)
• Intoxication (Blood Alcohol Percentage)
Categories:
22. 2230.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Unclear (?) modal categories e.g.:
• Emotion: ???
• Cf.: Disposition: Domain-Specific …. ?
• Cf.: Level of Interest (?)
Categories:
23. 2330.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Categorial Models of human emotion ...
... which can be utilized for automatic emotion recognition
• Two-Class models,
e.g. (not) cooperative
• Base Emotions [Ekman, 1992]
(Angriness, Disgust, Fear,
Joy, Sadness, Surprise, Neutral)
• VA(D) Models
(Valence (Pleasure) Arousal Dominance)
• Geneva Emotion Wheel
[Scherer, 2005]
2
3
24. 2430.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Categorial Models of human emotion (2):
enhanced listings
Siegert et al. 2011 ICME
2
4
• sadness,
• contempt,
• surprise,
• interest,
• hope,
• relief,
• joy,
• helplessness,
• confusion
25. 2530.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Categorial Models of human emotion (3):
Self-Assessment Manikins [Bradley, Lang, 1994]
Böck et al. 2011 ACII
2
5
26. 2630.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
27. 2730.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• (having fixed the modalities and categories)
• Examples; EXMARaLDA, FOLKER, ikannotate
EXMARaLDA: „Extensible Markup Language for Discourse Annotation“, www.exmaralda.org/, Hamburger Zentrum für Sprachkorpora (HZSK) und
SFB 538 ‘Multilingualism’, seit 2001/ 2006
FOLKER: „Forschungs- und Lehrkorpus Gesprochenes Deutsch“ - Transkriptionseditor, http://agd.ids-mannheim.de/folker.shtml, Institute for
German Language, Uni Mannheim, seit 2010
[Schmidt, Schütte, 2010]
Transcription / annotation tools
28. 2830.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
ikannotate - A Tool for Labelling, Transcription, and Annotation of Emotionally Coloured
Speech (2011)
• Otto von Guericke University - Chair of Cognitive Systems + Dept. of Psychosomatic Medicine
and Psychotherapy
ü Written in QT4 based on C++
ü Versions for Linux, Windows XP and higher, and Mac OS X
ü Sources and binaries are available on demand
ü Handles different output formats, especially, XML and TXT
ü Processes MP3 and WAV files
ü According to conversation analytic system of transcription
(GAT) (version 1 and 2) [Selting et.al., 2011]
http://ikannotate.cognitive-systems-magdeburg.de/
ikannotate tool
29. 2930.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Screenshots of ikannotate (I)
Böck et al. 2011 ACII
30. 3030.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Screenshots of ikannotate (II)
Böck et al. 2011 ACII
31. 3130.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
32. 3230.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Overview: http://emotion-research.net/wiki/Databases (not complete)
• Contains information on: Identifier, URL, Modalities, Emotional content,
Emotion elicitation methods, Size, Nature of material, Language
• Published overviews: Ververidis & Kotropoulos 2006, Schuller et al. 2010,
Appendix of [Pittermann et al.2010]*
• Popular corpora (listed on website above):
Emo-DB: Berlin Database of Emotional Speech 2005
SAL: Sensitive Artificial Listener (Semaine 2010)
(not listed on website above):
eNTERFACE (2005)
LMC: LAST MINUTE (2012)
Table Talk (2013)
Audio-Visual Interest Corpus (AVIC) (ISCA 2009)
• Ververidis, D. & Kotropoulos, C. (2006). “Emotional speech recognition: Resources, features, and methods”. Speech Commun 48 (9), pp.
1162–1181.
• Schuller, B.; Vlasenko, B.; Eyben, F.; Wollmer, M.; Stuhlsatz, A.; Wendemuth, A. & Rigoll, G. (2010). “Cross-Corpus Acoustic Emotion
Recognition: Variances and Strategies” IEEE Trans. Affect. Comput. 1 (2), pp. 119–131.
• Pittermann, J.; Pittermann, A. & Minker, W. (2010). Handling Emotions in Human-Computer Dialogues. Amsterdam, The Netherlands:
Springer.
Corpora of affective speech (+other modalities)
34. 3430.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Burkhardt, et al., 2005: A Database of German Emotional Speech,
• Proc. INTERSPEECH 2005, Lisbon, Portugal, 1517-1520.
• 7 emotions: anger, boredom, disgust, fear, joy, neutral, sadness
• 10 professional German actors, 5f, 494 phrases
• Perception test with 20 subjects: 84.3% mean acc.
• http://pascal.kgw.tu-berlin.de/emodb/index-1280.html
Example 1: Berlin Database of Emotional Speech (EMO-DB)
35. 3530.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Example 2: LAST MINUTE Corpus
Setup
Non-acted, emotions evoked by story:
task solving with difficulties (barriers)
Groups
N = 130, balanced in age, gender,
education
Duration 56:02:14
Sensors 13
Max. Video Bandwidth 1388x1038 25Hz
Biopsychological data heart beat, respiration, skin reductance
Questionnaires sociodemographic, psychometric
Interviews yes (73 subjects)
Language German
Available upon request at roesner@ovgu.de and joerg.frommer@med.ovgu.de
Frommer et al. 2012 LREC
36. 3630.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
37. 3730.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Remember, now you have transcribed/annotated data with fixed
categories (across modalities?) and modalities.
• You want to use that data to construct unimodal or multimodal
data-driven recognition engines
• Once you have these engines, you can automatically determine the
categories in yet unkown data.
Data-driven recognition engines
38. 3830.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• It’s Pattern Recognition
•
• Knowledge
Sources
•
A Unified View on data driven recognition
Schuller 2012 Cognitive Behavioural Systems
COST
( ){ }Llyx ll ,...,1, ==Λ
Capture
Pre-
processing
Feature
extraction
Feature
reduction
Classification
Regression
Decoding
U f(x') xx' y=κrf(x)
Feature
generation /
selection
multi-
layered multi-
layered
once
Dictionary
Interaction
Grammar
Production
Model
( ) κΩ→→ xxf
Encoding
Learner
Optimisation
39. 3930.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Audio Features
Böck et al. 2013 HCII
Facial Action Units
• MFCCs with Delta and Acceleration
• Prosodic features
• Formants and corresponding
bandwidths
• Intensity
• Pitch
• Jitter
• For acoustic feature extraction: Hidden Markov Toolkit
(HTK) and phonetic analysis software PRAAT (
http://www.praat.org)
40. 4030.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
What is the current state of affect recognition?
Table : Overview of reported results, #C: Number of Classes, eNT: eNTERFACE,
VAM: Vera am Mittag, SAL: Sensitive Artificial Listener, LMC: LAST MINUTE.
Comparing the results on acted emotional data and naturalistic
interactions:
• recognition performance decreases
• too much variability within the data
Database Result #C Comment Reference
emoDB
(acted)
91.5% 2 6552 acoustic features and GMMs Schuller et al., 2009
eNT
(primed)
74.9% 2 6552 acoustic features, GMMs Schuller et al., 2009
VAM
(natural)
76.5% 2 6552 acoustic features with GMMs Schuller et al., 2009
SAL
(natural)
61.2% 2 6552 acoustic features with GMMs Schuller et al., 2009
LMC
(natural)
80% 2 pre-classification of visual, acoustic
and gestural features, MFN
Krell et al.,2013
Siegert et al. 2013 ERM4HCI
41. 4130.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
User-group / temporal specific affect recognition
SuccessRates [stress/nostress](testedon LASTMINUTEcorpus):
• 72% utilizing (few) group-specific (young/old+male/female)
audio features [Siegert et al., 2013]
• 71% utilizing audio-visual features and a linear filter as decision level
fusion [Panning et al., 2012]
• 80% using facial expressions, gestural analysis and acoustic features
with Markov Fusion Networks [Krell et al., 2013]
Approaches2&3integrate their classifiers of longer temporal sequences.
Siegert et al. 2013 ERM4HCI, workshop ICMI 2013
42. 4230.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Classification Engines – Cross-Modalities
• Classification based
on audio feature
• Preselection of
relevant video
sequences
• Manual annotation of
Action Units and
classification of facial
expressions
Further:
• preclassification of
the sequences
• Dialog act
representation models
Böck et al. 2013 HCII, Friesen et al. 2014 LREC
43. 4330.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
1. Affective Factors in Man-Machine-Interaction
2. Speech and multimodal sensor data – what they reveal
3. Discrete or dimensional affect description
4. software-supported affect annotation
5. Corpora
6. Automatic emotion recognition
7. Applications in companion systems and in dialog control
Contents
44. 4430.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Remember, now you have transcribed/annotated data with fixed
categories (across modalities?) and modalities (maybe a corpus).
• You also have a categories classifier trained on these data, i.e.
domain specific / person specific.
Now we use categorized information in applications:
Usage of multimodal information
45. 4530.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
• Disambiguation (saying and pointing)
• Person‘s choice (talking is easier than typing)
• „Real“ information (jokes from a blushing person?)
• Robustness (talking obscured by noise, but lipreading works)
• Higher information content (multiple congruent modalities)m
• Uniqueness (reliable emotion recognition only from multi-
modalities)
Why more modalities help understanding what a
person wants to „tell“
46. 4630.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Companion Technology
Applica'on
/
Dialog-‐
Management
Input signalSpeech
Gesture
Touch
Physiolog.
Sensor
Devices
Mul'modal
Components
Output signal
Multimodal
Adaptive
Individualised
Interaction
Management
User
Weber et al. 2012 SFB TRR
62
47. 4730.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Recognition of critical dialogue courses
• On basis of linguistic content
• in combination with multi-modal emotion recognition
Development of empathy-promoting dialogue strategies
• Motivation of the user
• Prevent abandonment of the dialogue in problem-prone situations
Emotional and dialogic conditions in user behavior
49. 4930.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
Take home messages / outlook
Emotion / Affect recognition:
• Data driven, automatic pattern recognition
• Categorisation, Annotation tools
• Temporal emotion train dependent on mood and
personality
• Outlook:
Emo'on-‐categorial
Appraisal-‐Model
Use in Man-Machine-Interaction:
• Early detection / counteraction of adverse dialogs
• Outlook:
use
in
call
centers
and
companion
technology
50. 5030.Oct. 2014YAC - Automatic recognition of emotions in speech – Andreas Wendemuth
… thank you!
www.cogsy.de