This document discusses speech-based emotion recognition using Gaussian mixture models (GMM). GMMs are statistical models that are well-suited for developing emotion recognition systems from large feature datasets. The document proposes using GMMs trained on excitation features extracted from speech signals to classify emotions into categories like happy, angry, sad, and neutral. It describes extracting excitation source features through linear predictive coding analysis to capture information about a speaker's vocal excitation source. The goal is to develop a GMM-based emotion recognition system that can classify emotions in conversations.
This document discusses an analysis of an emotion recognition system through speech signals using K-nearest neighbors (KNN) and Gaussian mixture model (KNN) classifiers. It provides background on the challenges of automatic emotion recognition from speech and describes common features extracted from speech like mel frequency cepstrum coefficients and prosodic features. The document outlines the process of an emotion recognition system including feature extraction, training classifiers on a speech database, and classifying emotions. It then gives more detail on the KNN and GMM classifiers and how they were used to classify six emotional states from the Berlin emotional speech database.
This document summarizes a research paper on classifying speech using Mel frequency cepstrum coefficients (MFCC) and power spectrum analysis. The paper reviews different classifiers used for speech emotion recognition, including neural networks, Gaussian mixture models, and support vector machines. It proposes using MFCC and power spectrum features as inputs to an artificial neural network classifier to identify emotions in speech, such as anger, happiness, sadness, and neutral states. Testing is performed on emotional speech samples to evaluate the performance and limitations of the proposed speech emotion recognition system.
This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of natural language processing and its applications. The document then discusses the need for sentiment analysis in areas like artificial intelligence. It proceeds to compare different techniques for emotion extraction from text, including text mining, empirical studies, emotion extraction engines, vector space models, and emotion markup languages. For each technique, it outlines the general approach and provides examples or tables to illustrate how emotions can be identified from text. However, it notes that current applications have not achieved 100% accuracy in realistic sentiment analysis.
Emotion Recognition Based On Audio SpeechIOSR Journals
This document summarizes a research paper on emotion recognition based on audio speech. It discusses how acoustic features are extracted from speech signals by applying preprocessing techniques like preemphasis and framing. It describes extracting features like Mel frequency cepstral coefficients (MFCCs) that capture characteristics of the vocal tract. Support vector machines (SVMs) are used as pattern classification methods to build models for each emotion and compare test speech features to recognize emotions. The paper confirms the advantage of its audio-based emotion recognition approach through experimental results and discusses potential improvements and future work on increasing efficiency and recognizing emotion intensity.
This document discusses a proposed system for classifying audio scenes in action movies. It aims to provide scene recognition and detection by separating audio classes and obtaining better sound classification accuracy. The system extracts audio features like zero-crossing rate, short-time energy, volume root mean square, and volume dynamic range. It then uses hidden Markov models and support vector machines to classify audio scenes, labeling them as happy, miserable, or action scenes. Sound event types classified include gunshots, screams, car crashes, talking, laughter, fighting, shouting, and background crowd noise. The goal is to index and retrieve interesting events from action movies to engage viewers.
Speech Emotion Recognition by Using Combinations of Support Vector Machine (S...mathsjournal
This document summarizes a research paper that evaluates different classification methods for speech emotion recognition, including Support Vector Machine (SVM), C5.0, and a combination of SVM and C5.0 (SVM-C5.0). The paper extracts features like energy, zero crossing rate, pitch, and MFCCs from speech samples in the Berlin Emotional Speech Database, which contains utterances expressing seven emotions. These features are classified using SVM, C5.0, and SVM-C5.0, and the results show that SVM-C5.0 performs best, achieving recognition rates between 5.5-8.9% higher than SVM or C5.0 alone depending on the number of emotions.
This document describes a student project on speech-based emotion recognition. The project uses convolutional neural networks (CNN) and mel-frequency cepstral coefficients (MFCC) to classify emotions in speech into categories like happy, sad, fearful, calm and angry. The proposed system provides advantages over existing systems by allowing variable length audio inputs, faster processing, and real-time classification of more emotion categories. It achieves a test accuracy of 91.04% according to the document.
IRJET- Emotion recognition using Speech Signal: A ReviewIRJET Journal
This document provides a review of speech emotion recognition techniques. It discusses how speech emotion recognition systems work, including common features extracted from speech like MFCCs and LPC coefficients. Classification techniques used in these systems are also examined, such as DTW, ANN, GMM, and K-NN. The document concludes that speech emotion recognition could be useful for applications requiring natural human-computer interaction, like car systems that monitor driver emotion or educational tutorials that adapt based on student emotion.
This document discusses an analysis of an emotion recognition system through speech signals using K-nearest neighbors (KNN) and Gaussian mixture model (KNN) classifiers. It provides background on the challenges of automatic emotion recognition from speech and describes common features extracted from speech like mel frequency cepstrum coefficients and prosodic features. The document outlines the process of an emotion recognition system including feature extraction, training classifiers on a speech database, and classifying emotions. It then gives more detail on the KNN and GMM classifiers and how they were used to classify six emotional states from the Berlin emotional speech database.
This document summarizes a research paper on classifying speech using Mel frequency cepstrum coefficients (MFCC) and power spectrum analysis. The paper reviews different classifiers used for speech emotion recognition, including neural networks, Gaussian mixture models, and support vector machines. It proposes using MFCC and power spectrum features as inputs to an artificial neural network classifier to identify emotions in speech, such as anger, happiness, sadness, and neutral states. Testing is performed on emotional speech samples to evaluate the performance and limitations of the proposed speech emotion recognition system.
This document discusses issues in sentiment analysis and emotion extraction from text. It provides an overview of natural language processing and its applications. The document then discusses the need for sentiment analysis in areas like artificial intelligence. It proceeds to compare different techniques for emotion extraction from text, including text mining, empirical studies, emotion extraction engines, vector space models, and emotion markup languages. For each technique, it outlines the general approach and provides examples or tables to illustrate how emotions can be identified from text. However, it notes that current applications have not achieved 100% accuracy in realistic sentiment analysis.
Emotion Recognition Based On Audio SpeechIOSR Journals
This document summarizes a research paper on emotion recognition based on audio speech. It discusses how acoustic features are extracted from speech signals by applying preprocessing techniques like preemphasis and framing. It describes extracting features like Mel frequency cepstral coefficients (MFCCs) that capture characteristics of the vocal tract. Support vector machines (SVMs) are used as pattern classification methods to build models for each emotion and compare test speech features to recognize emotions. The paper confirms the advantage of its audio-based emotion recognition approach through experimental results and discusses potential improvements and future work on increasing efficiency and recognizing emotion intensity.
This document discusses a proposed system for classifying audio scenes in action movies. It aims to provide scene recognition and detection by separating audio classes and obtaining better sound classification accuracy. The system extracts audio features like zero-crossing rate, short-time energy, volume root mean square, and volume dynamic range. It then uses hidden Markov models and support vector machines to classify audio scenes, labeling them as happy, miserable, or action scenes. Sound event types classified include gunshots, screams, car crashes, talking, laughter, fighting, shouting, and background crowd noise. The goal is to index and retrieve interesting events from action movies to engage viewers.
Speech Emotion Recognition by Using Combinations of Support Vector Machine (S...mathsjournal
This document summarizes a research paper that evaluates different classification methods for speech emotion recognition, including Support Vector Machine (SVM), C5.0, and a combination of SVM and C5.0 (SVM-C5.0). The paper extracts features like energy, zero crossing rate, pitch, and MFCCs from speech samples in the Berlin Emotional Speech Database, which contains utterances expressing seven emotions. These features are classified using SVM, C5.0, and SVM-C5.0, and the results show that SVM-C5.0 performs best, achieving recognition rates between 5.5-8.9% higher than SVM or C5.0 alone depending on the number of emotions.
This document describes a student project on speech-based emotion recognition. The project uses convolutional neural networks (CNN) and mel-frequency cepstral coefficients (MFCC) to classify emotions in speech into categories like happy, sad, fearful, calm and angry. The proposed system provides advantages over existing systems by allowing variable length audio inputs, faster processing, and real-time classification of more emotion categories. It achieves a test accuracy of 91.04% according to the document.
IRJET- Emotion recognition using Speech Signal: A ReviewIRJET Journal
This document provides a review of speech emotion recognition techniques. It discusses how speech emotion recognition systems work, including common features extracted from speech like MFCCs and LPC coefficients. Classification techniques used in these systems are also examined, such as DTW, ANN, GMM, and K-NN. The document concludes that speech emotion recognition could be useful for applications requiring natural human-computer interaction, like car systems that monitor driver emotion or educational tutorials that adapt based on student emotion.
This document summarizes a paper on multimodal emotion recognition from speech, text, and video data. It discusses how combining multiple modalities can provide richer information than single modalities alone. It presents the IEMOCAP and CMU-MOSEI datasets and compares their modalities. Techniques for fusing modalities include early and late fusion. The paper proposes a solution that filters ineffective data, regenerates proxy features, and uses multiplicative fusion to boost stronger modalities. It evaluates the approach on the CMU-MOSEI dataset using speech, text, and video features and discusses limitations in distinguishing some emotions.
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
This document presents research on developing an Arabic speech emotion recognition system using a convolutional neural network (CNN) model. The researchers propose a model called ASERS-CNN and evaluate it on an Arabic speech dataset containing recordings of 4 emotions. Their results show the ASERS-CNN achieves 98.18% accuracy, outperforming their previous ASERS-LSTM model which achieved 97.44% accuracy. They also find that using 5 acoustic feature types and 50 training epochs leads to the best ASERS-CNN performance of 98.52% accuracy.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 23d
Abstract. In modern days synthesis of human images and videos is arguably one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but deeply bonded to the mentioned topic. Since the publication of WaveNet paper by Google researchers in 2016, the state-of-the-art approach transferred from parametric and concatenative systems to deep learning models. Most of the work on the area focuses on improving the intelligibility and naturalness of the speech. However, almost every significant study also mentions ways to generate speech with the voices of different speakers. Usually, such an enhancement requires the model’s re-training in case of generating audio with the voice of a speaker that was not present in the training set. Additionally, studies focused on highly modular speech generation are rare. Therefore there is a room left for research on ways to add new parameters for other aspects of the speech, like sentiment, prosody, and melody. In this work, we aimed to implement a competitive text-to-speech solution with the ability to specify the speaker without model re-training and explore possibilities for adding emotions to the generated speech. Our approach generates good quality speech with the mean opinion score of 3,78 (out of 5) points and the ability to mimic speaker voice in real-time, which is a big improvement over the baseline that merely obtains 2,08. On top of that, we researched sentiment representation possibilities. We built an emotion classifier that performs on the level of the current state of the art solutions by giving an accuracy of more than eighty percent.
A critical insight into multi-languages speech emotion databasesjournalBEEI
With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.
Ai based character recognition and speech synthesisAnkita Jadhao
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
This is the presentation of our IEEE ICASSP 2021 paper "seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset".
The document summarizes Kun Zhou's PhD research on emotional voice conversion with non-parallel data at the National University of Singapore. It introduces emotional voice conversion and its challenges, including the lack of parallel training data. It then summarizes Kun's publications, which propose CycleGAN-based and VAW-GAN approaches to model prosody for speaker-dependent and independent emotional voice conversion. One publication introduces a method for transferring both seen and unseen emotional styles using a pre-trained speech emotion recognizer to describe emotional styles.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
VAW-GAN for disentanglement and recomposition of emotional elements in speechKunZhou18
- The document describes a framework for emotional voice conversion using VAW-GAN that can disentangle and recompose emotional elements in speech. It proposes using VAW-GAN with continuous wavelet transform to model prosody and decompose fundamental frequency into different time scales. Conditioning the decoder on fundamental frequency is shown to improve emotion conversion performance. Experiments demonstrate the effectiveness of the approach on an English emotional speech database.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using prosodic information from spoken utterances to determine a speaker's level of certainty. It analyzed student responses to questions about an operating systems lecture. The responses were annotated as certain, uncertain, or neutral based on audio and lexical analysis. Acoustic-prosodic features were then extracted from the utterances using software and normalized. The goal is to use prosodic features to help dialogue systems understand the user's mental state and frame responses accordingly.
Signal Processing Tool for Emotion Recognitionidescitation
In the course of realization of modern day robots,
which not only perform tasks, but also behaves like human
beings during their interaction with the natural environment,
it is essential for us to impart knowledge of the underlying
emotions in the spoken utterances of human beings to the
robots, enabling them to be consistent, whole, complete and
perfect. To this end, it is essential for them too to understand
and identify the human emotions. For this reason, stress is
laid now-a-days on the study of emotional content of the speech
and accordingly speech emotion recognition engines have been
proposed. This paper is a survey of the main aspects of speech
emotion recognition, namely, features extractions and types
of features commonly used, selection of most informed
features from the original dataset of the features, and
classification of the features according to different classifying
techniques based on relative information regarding commonly
used database for the speech emotion recognition.
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
The document discusses automatic speech recognition (ASR) and its components. It covers two main approaches to ASR - template matching and feature analysis. Template matching uses pre-recorded templates to match voices, while feature analysis processes voices into features using techniques like linear predictive coding to determine similarities between expected and actual input. The document also describes the process of speech recognition including acoustic wave form analysis, feature extraction, and matching phonemes, words and sentences.
Speech Emotion Recognition is a recent research topic in the Human Computer Interaction (HCI) field. The need has risen for a more natural communication interface between humans and computer, as computers have become an integral part of our lives. A lot of work currently going on to improve the interaction between humans and computers. To achieve this goal, a computer would have to be able to distinguish its present situation and respond differently depending on that observation. Part of this process involves understanding a user‟s emotional state. To make the human computer interaction more natural, the objective is that computer should be able to recognize emotional states in the same as human does. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions. The proposed system aims at identification of basic emotional states such as anger, joy, neutral and sadness from human speech. While classifying different emotions, features like MFCC (Mel Frequency Cepstral Coefficient) and Energy is used. In this paper, Standard Emotional Database i.e. English Database is used which gives the satisfactory detection of emotions than recorded samples of emotions. This methodology describes and compares the performances of Learning Vector Quantization Neural Network (LVQ NN), Multiclass Support Vector Machine (SVM) and their combination for emotion recognition.
Speech Recognition in Artificail InteligenceIlhaan Marwat
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice commands. It works by converting analog audio to digital signals, separating speech from background noise, and analyzing phonetic patterns to recognize words. There are two main types - speaker-dependent software requires training a user's voice, while speaker-independent software can recognize any voice without training but is generally less accurate. Speech recognition has applications in fields like military operations, navigation systems, radiology, and call centers. It offers advantages for people with disabilities but also faces challenges from variations in human speech and filtering noise. The technology continues to improve with advances in processing power and algorithms.
Emotional analysis and evaluation of kannada speech databaseIAEME Publication
This document summarizes a study on developing and analyzing an emotional speech database in Kannada language. Key aspects analyzed include pitch, intensity, percentage of unvoiced frames, sound pressure, vocal tract variations and spectrograms of different emotions. Linear Predictive Coding was used to extract features from the speech samples. The database was evaluated using Mean Opinion Score from human listeners and classification accuracy from Probabilistic Neural Network and K-Nearest Neighbors algorithms. The analysis found differences in various acoustic parameters across emotions like pitch being highest in fear and lowest in sadness. This database can help build effective emotion recognition systems in Kannada.
Upgrading the Performance of Speech Emotion Recognition at the Segmental Level IOSR Journals
This document presents research on improving the accuracy of automatic speech emotion recognition using minimal inputs and features. The researchers used only the vowel formants from English speech recordings of 10 female speakers producing neutral and 6 basic emotions. They analyzed the vowel formants using statistical analysis and 3 classifiers to identify the best performing formants. An artificial neural network using the selected formant values achieved 95.6% accuracy in classifying emotions, higher than previous studies. The approach requires fewer features and less complex processing while achieving good recognition rates.
This document summarizes a paper on multimodal emotion recognition from speech, text, and video data. It discusses how combining multiple modalities can provide richer information than single modalities alone. It presents the IEMOCAP and CMU-MOSEI datasets and compares their modalities. Techniques for fusing modalities include early and late fusion. The paper proposes a solution that filters ineffective data, regenerates proxy features, and uses multiplicative fusion to boost stronger modalities. It evaluates the approach on the CMU-MOSEI dataset using speech, text, and video features and discusses limitations in distinguishing some emotions.
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
This document presents research on developing an Arabic speech emotion recognition system using a convolutional neural network (CNN) model. The researchers propose a model called ASERS-CNN and evaluate it on an Arabic speech dataset containing recordings of 4 emotions. Their results show the ASERS-CNN achieves 98.18% accuracy, outperforming their previous ASERS-LSTM model which achieved 97.44% accuracy. They also find that using 5 acoustic feature types and 50 training epochs leads to the best ASERS-CNN performance of 98.52% accuracy.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
The document discusses speech emotion recognition using machine learning. It aims to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. It extracts MFCC, mel spectrogram, and chroma features from the dataset and uses an MLP classifier to classify emotions into 8 categories with an accuracy of 66.67%. The model works best at identifying calm emotions and gets confused between similar emotions. Future work could explore using larger datasets with CNN, RNN models on different speakers and accents.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 23d
Abstract. In modern days synthesis of human images and videos is arguably one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but deeply bonded to the mentioned topic. Since the publication of WaveNet paper by Google researchers in 2016, the state-of-the-art approach transferred from parametric and concatenative systems to deep learning models. Most of the work on the area focuses on improving the intelligibility and naturalness of the speech. However, almost every significant study also mentions ways to generate speech with the voices of different speakers. Usually, such an enhancement requires the model’s re-training in case of generating audio with the voice of a speaker that was not present in the training set. Additionally, studies focused on highly modular speech generation are rare. Therefore there is a room left for research on ways to add new parameters for other aspects of the speech, like sentiment, prosody, and melody. In this work, we aimed to implement a competitive text-to-speech solution with the ability to specify the speaker without model re-training and explore possibilities for adding emotions to the generated speech. Our approach generates good quality speech with the mean opinion score of 3,78 (out of 5) points and the ability to mimic speaker voice in real-time, which is a big improvement over the baseline that merely obtains 2,08. On top of that, we researched sentiment representation possibilities. We built an emotion classifier that performs on the level of the current state of the art solutions by giving an accuracy of more than eighty percent.
A critical insight into multi-languages speech emotion databasesjournalBEEI
With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.
Ai based character recognition and speech synthesisAnkita Jadhao
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
This is the presentation of our IEEE ICASSP 2021 paper "seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset".
The document summarizes Kun Zhou's PhD research on emotional voice conversion with non-parallel data at the National University of Singapore. It introduces emotional voice conversion and its challenges, including the lack of parallel training data. It then summarizes Kun's publications, which propose CycleGAN-based and VAW-GAN approaches to model prosody for speaker-dependent and independent emotional voice conversion. One publication introduces a method for transferring both seen and unseen emotional styles using a pre-trained speech emotion recognizer to describe emotional styles.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
VAW-GAN for disentanglement and recomposition of emotional elements in speechKunZhou18
- The document describes a framework for emotional voice conversion using VAW-GAN that can disentangle and recompose emotional elements in speech. It proposes using VAW-GAN with continuous wavelet transform to model prosody and decompose fundamental frequency into different time scales. Conditioning the decoder on fundamental frequency is shown to improve emotion conversion performance. Experiments demonstrate the effectiveness of the approach on an English emotional speech database.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using prosodic information from spoken utterances to determine a speaker's level of certainty. It analyzed student responses to questions about an operating systems lecture. The responses were annotated as certain, uncertain, or neutral based on audio and lexical analysis. Acoustic-prosodic features were then extracted from the utterances using software and normalized. The goal is to use prosodic features to help dialogue systems understand the user's mental state and frame responses accordingly.
Signal Processing Tool for Emotion Recognitionidescitation
In the course of realization of modern day robots,
which not only perform tasks, but also behaves like human
beings during their interaction with the natural environment,
it is essential for us to impart knowledge of the underlying
emotions in the spoken utterances of human beings to the
robots, enabling them to be consistent, whole, complete and
perfect. To this end, it is essential for them too to understand
and identify the human emotions. For this reason, stress is
laid now-a-days on the study of emotional content of the speech
and accordingly speech emotion recognition engines have been
proposed. This paper is a survey of the main aspects of speech
emotion recognition, namely, features extractions and types
of features commonly used, selection of most informed
features from the original dataset of the features, and
classification of the features according to different classifying
techniques based on relative information regarding commonly
used database for the speech emotion recognition.
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
The document discusses automatic speech recognition (ASR) and its components. It covers two main approaches to ASR - template matching and feature analysis. Template matching uses pre-recorded templates to match voices, while feature analysis processes voices into features using techniques like linear predictive coding to determine similarities between expected and actual input. The document also describes the process of speech recognition including acoustic wave form analysis, feature extraction, and matching phonemes, words and sentences.
Speech Emotion Recognition is a recent research topic in the Human Computer Interaction (HCI) field. The need has risen for a more natural communication interface between humans and computer, as computers have become an integral part of our lives. A lot of work currently going on to improve the interaction between humans and computers. To achieve this goal, a computer would have to be able to distinguish its present situation and respond differently depending on that observation. Part of this process involves understanding a user‟s emotional state. To make the human computer interaction more natural, the objective is that computer should be able to recognize emotional states in the same as human does. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions. The proposed system aims at identification of basic emotional states such as anger, joy, neutral and sadness from human speech. While classifying different emotions, features like MFCC (Mel Frequency Cepstral Coefficient) and Energy is used. In this paper, Standard Emotional Database i.e. English Database is used which gives the satisfactory detection of emotions than recorded samples of emotions. This methodology describes and compares the performances of Learning Vector Quantization Neural Network (LVQ NN), Multiclass Support Vector Machine (SVM) and their combination for emotion recognition.
Speech Recognition in Artificail InteligenceIlhaan Marwat
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice commands. It works by converting analog audio to digital signals, separating speech from background noise, and analyzing phonetic patterns to recognize words. There are two main types - speaker-dependent software requires training a user's voice, while speaker-independent software can recognize any voice without training but is generally less accurate. Speech recognition has applications in fields like military operations, navigation systems, radiology, and call centers. It offers advantages for people with disabilities but also faces challenges from variations in human speech and filtering noise. The technology continues to improve with advances in processing power and algorithms.
Emotional analysis and evaluation of kannada speech databaseIAEME Publication
This document summarizes a study on developing and analyzing an emotional speech database in Kannada language. Key aspects analyzed include pitch, intensity, percentage of unvoiced frames, sound pressure, vocal tract variations and spectrograms of different emotions. Linear Predictive Coding was used to extract features from the speech samples. The database was evaluated using Mean Opinion Score from human listeners and classification accuracy from Probabilistic Neural Network and K-Nearest Neighbors algorithms. The analysis found differences in various acoustic parameters across emotions like pitch being highest in fear and lowest in sadness. This database can help build effective emotion recognition systems in Kannada.
Upgrading the Performance of Speech Emotion Recognition at the Segmental Level IOSR Journals
This document presents research on improving the accuracy of automatic speech emotion recognition using minimal inputs and features. The researchers used only the vowel formants from English speech recordings of 10 female speakers producing neutral and 6 basic emotions. They analyzed the vowel formants using statistical analysis and 3 classifiers to identify the best performing formants. An artificial neural network using the selected formant values achieved 95.6% accuracy in classifying emotions, higher than previous studies. The approach requires fewer features and less complex processing while achieving good recognition rates.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
Detection of speech under stress using spectral analysiseSAT Journals
Abstract This paper deals with an approach to the detection of speech in English language. The stress detection is necessary which provides real time information of state of mind of a person. Voice features from the speech signal is influenced by stress is MFCC is considered is this paper. To examine the effect of Exam-Stress on speech production an experiment was designed. First Year students of age group 18 to 20 were selected and assignment was given to them and instructs them that have viva on that assignment and their performance in the viva will decide their final internal marks in the examination. The experiment and the analysis of the test results are reported in this paper. Keywords: Speech, Stress, Spectral Analysis, Discrete Wavelet Transform, Artificial Neural Network.
This document lists over 100 potential IEEE paper titles across various domains including affective computing, biology, cloud computing, dependable and secure computing, knowledge and data engineering, mobile computing, multimedia, networking, parallel and distributed systems, services computing, software engineering, speech recognition, and visualization and computer graphics. It also provides information about project support for registered students, including an IEEE base paper, abstract document, source code, and assistance with international publications. The document serves as a reference for students pursuing IEEE paper projects.
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 provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document summarizes a research paper that proposes a method for emotion identification in continuous speech using cepstral analysis and generalized gamma mixture modeling. The key contributions are:
1) It extracts MFCC and LPC features from speech signals to model emotions like happy, angry, boredom and sad.
2) It uses a generalized gamma distribution instead of GMM for more accurate feature extraction and classification, as GGD can model speech signal variations better.
3) An experiment is conducted on a database of 50 speakers' speech in 5 emotions, achieving over 90% recognition accuracy using the proposed MFCC-LPC features and GGD modeling.
Signal & Image Processing : An International Journal sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
A Review Paper on Speech Based Emotion Detection Using Deep LearningIRJET Journal
This document reviews techniques for speech-based emotion detection using deep learning. It discusses how deep learning techniques have been proposed as an alternative to traditional methods for speech emotion recognition. Feature extraction is an important part of speech emotion recognition, and deep learning can help minimize the complexity of extracted features. The document surveys related work on speech emotion recognition using techniques like deep neural networks, convolutional neural networks, recurrent neural networks, and more. It examines the limitations of current approaches and the potential for deep learning to improve speech-based emotion detection.
Novel Methodologies for Classifying Gender and Emotions Using Machine Learnin...BRIGHT WORLD INNOVATIONS
This document proposes a system for classifying gender and emotions from human voice using machine learning algorithms. It involves preprocessing voice data, removing noise using hidden Markov models, extracting features using discrete wavelet transforms, and classifying gender and emotion using k-nearest neighbors. The system combines gender and emotion classification into a single system, achieving higher accuracy than systems that only classify one or the other. Evaluation shows the proposed system achieves 97% accuracy, outperforming existing systems with accuracies of 75-86%.
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 summarizes research on speaker recognition in noisy environments. It begins with an introduction discussing the goals of speaker identification and verification and their applications. It then provides details on the basic components of a speaker recognition system, including feature extraction and classification. The document focuses on methods for modeling noise, including generating multiple noisy training conditions and focusing matching on unaffected features. Experimental results are shown through snapshots of a prototype system interface that allows adding and recognizing speakers based on voice samples. The system is able to identify speakers in the presence of noise by comparing features to stored codebooks generated during training.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
This document proposes and tests a method for emotional speech synthesis using prosody adaptation. It begins by conducting perceptual tests to show that manipulating prosody features alone can convey emotional meaning to some degree. It then presents an adaptation algorithm to model linguistic and emotional prosody components separately, requiring only a small amount of emotional training data. Experiments synthesizing Mandarin sadness and happiness using this method achieve over 80% accuracy in emotion identification by listeners. The results suggest this approach could flexibly synthesize subtle emotions with limited training data by adapting prosody features.
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.
SPEECH EMOTION RECOGNITION SYSTEM USING RNNIRJET Journal
This document discusses a speech emotion recognition system using recurrent neural networks (RNNs). It begins with an abstract describing speech emotion recognition and its importance. Then it provides background on speech emotion databases, feature extraction using MFCC, and classification approaches like RNNs. It reviews related work on speech emotion recognition using various methods. Finally, it concludes that MFCC feature extraction and RNN classification was used in the proposed system to take advantage of their performance in machine learning applications. The system aims to help machines understand human interaction and respond based on the user's emotion.
Efficient Speech Emotion Recognition using SVM and Decision TreesIRJET Journal
This document discusses efficient speech emotion recognition using support vector machines and decision trees. It summarizes a research paper that extracted speech features like variance, standard deviation, energy and pitch from an emotional speech corpus containing 535 speech segments expressing seven emotions. The extracted features were used to train and test an SVM classifier for emotion recognition. The classifier achieved an average accuracy of 85% across training and test sets at recognizing the seven emotions. Feature selection techniques were used to address the curse of dimensionality caused by the large number of extracted features.
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.
ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Modelsipij
When two people are on the phone, although they cannot observe the other person's facial expression and
physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical
care, if the emotional state of a patient, especially a patient with an expression disorder, can be known,
different care measures can be made according to the patient's mood to increase the amount of care. The
system that capable for recognize the emotional states of human being from his speech is known as Speech
emotion recognition system (SER). Deep learning is one of most technique that has been widely used in
emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion
recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN
model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech
corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and
new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is
outperformed ASERS-LSTM model where it get 98.18% accuracy.
Signal & Image Processing : An International Journalsipij
This document presents research on developing an Arabic speech emotion recognition system using a convolutional neural network (CNN) model. The researchers propose a model called ASERS-CNN and evaluate it on an Arabic speech dataset containing recordings of 4 emotions. Their best performing model achieves 98.52% accuracy using 5 acoustic features extracted from the speech and preprocessed with normalization and silence removal. They compare this result to their previous ASERS-LSTM model and other state-of-the-art methods, finding that ASERS-CNN outperforms ASERS-LSTM and existing approaches.
This paper introduces new features based on histograms of MFCC extracted from audio files to improve emotion recognition from speech. Experimental results on the Berlin and PAU databases using SVM and Random Forest classifiers show the proposed features achieve better classification results than current methods. Detailed analysis is provided on speech type (acted vs natural) and gender.
A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledg...ijtsrd
Suctioning is a common procedure performed by nurses to maintain the gas exchange, adequate oxygenation and alveolar ventilation in critical ill patients under mechanical ventilation and aim of this research is to provide knowledge regarding maintaining airway patency with suctioning care that will help in the implementation of the quality of nursing care, eventually it will lead to better results. The planned study is a pre experimental study to assess the effectiveness of planned teaching programme on knowledge regarding airway patency on patients with mechanical ventilator among the B.Sc. internship students of selected college of nursing at Moradabad. To assess the level of knowledge regarding maintaining airway patency in patients with mechanical ventilator among B.Sc. Nursing internship students. To assess the effectiveness of planned teaching programme in term of knowledge regarding airway patency among B.Sc. nursing internship students. The purpose of this study is to examine the association between knowledge and effectiveness regarding airway patency among B.Sc. Nursing internship demographic students and their selected partner variables. A pre experimental study was conducted among 86 participants, selected by non probability convenient sampling method. Demographic Performa and self structured questionnaire was used to collect the data from the B.Sc. internship students. Nafees Ahmed | Sana Usmani "A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledge Regarding Maintaining Airway Patency in Patients with Mechanical Ventilator" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47917.pdf Paper URL: https://www.ijtsrd.com/medicine/nursing/47917/a-study-to-assess-the-effectiveness-of-planned-teaching-programme-on-knowledge-regarding-maintaining-airway-patency-in-patients-with-mechanical-ventilator/nafees-ahmed
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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.
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.
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.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.