This document summarizes a research paper on understanding and estimating emotional expression using acoustic analysis of natural speech. The paper explores identifying seven emotional states (anger, surprise, sadness, happiness, fear, disgust, and neutral) using fifteen acoustic features extracted from the SAVEE speech database. Three models using different combinations of features were evaluated using various machine learning algorithms. The results showed that Model 2, using energy intensity, pitch, standard deviation, jitter, and shimmer, achieved the highest classification accuracy. Estimation of emotions using confidence intervals showed that most emotions could be accurately estimated using energy intensity and pitch. The paper concludes that expanding the study to include more features and databases could improve emotional state recognition.
Significance of Speech Intelligibility Assessors in Medium Classroom Using An...TELKOMNIKA JOURNAL
When there are constraints on the resources-equipment, manpower and time-to conduct speech
intelligibility tests, the most reliable or significant SI assessor for many different types of rooms is always
sought for. The purpose of this study was to determine the most significant speech intelligibility assessor in
four medium classrooms. The speech intelligibility assessors tested were RT60, C50, D50, and STIPA.
The data were acquired by means of sound recorder that recorded six Malay words spoken by a trained
male speaker, in four medium classrooms.The recorded speech signals were analyzed by DIRAC
software. The data of four speech intelligibility assessors have to be normalized before it can be analyzed
by AHP. In conclusion, C50 has shown the most consistent prediction of speech intelligibility in all sampled
classrooms. On the other hand, as the room gets larger, RT60 becomes significant for determining
speech intelligibility in these sampled classrooms.
OPTIMIZATION OF CROSS DOMAIN SENTIMENT ANALYSIS USING SENTIWORDNETijfcstjournal
The task of sentiment analysis of reviews is carried out using manually built / automatically generated
lexicon resources of their own with which terms are matched with lexicon to compute the term count for
positive and negative polarity. On the other hand the Sentiwordnet, which is quite different from other
lexicon resources that gives scores (weights) of the positive and negative polarity for each word. The
polarity of a word namely positive, negative and neutral have the score ranging between 0 to 1 indicates
the strength/weight of the word with that sentiment orientation. In this paper, we show that using the
Sentiwordnet, how we could enhance the performance of the classification at both sentence and document
level.
Significance of Speech Intelligibility Assessors in Medium Classroom Using An...TELKOMNIKA JOURNAL
When there are constraints on the resources-equipment, manpower and time-to conduct speech
intelligibility tests, the most reliable or significant SI assessor for many different types of rooms is always
sought for. The purpose of this study was to determine the most significant speech intelligibility assessor in
four medium classrooms. The speech intelligibility assessors tested were RT60, C50, D50, and STIPA.
The data were acquired by means of sound recorder that recorded six Malay words spoken by a trained
male speaker, in four medium classrooms.The recorded speech signals were analyzed by DIRAC
software. The data of four speech intelligibility assessors have to be normalized before it can be analyzed
by AHP. In conclusion, C50 has shown the most consistent prediction of speech intelligibility in all sampled
classrooms. On the other hand, as the room gets larger, RT60 becomes significant for determining
speech intelligibility in these sampled classrooms.
OPTIMIZATION OF CROSS DOMAIN SENTIMENT ANALYSIS USING SENTIWORDNETijfcstjournal
The task of sentiment analysis of reviews is carried out using manually built / automatically generated
lexicon resources of their own with which terms are matched with lexicon to compute the term count for
positive and negative polarity. On the other hand the Sentiwordnet, which is quite different from other
lexicon resources that gives scores (weights) of the positive and negative polarity for each word. The
polarity of a word namely positive, negative and neutral have the score ranging between 0 to 1 indicates
the strength/weight of the word with that sentiment orientation. In this paper, we show that using the
Sentiwordnet, how we could enhance the performance of the classification at both sentence and document
level.
Several attempts had been made to analyze emotion words in the fields of linguistics, psychology and sociology; with the advent of computers, the analyses of these words have taken a different dimension. Unfortunately, limited attempts have so far been made to using interval type-2 fuzzy logic (IT2FL) to analyze these words in native languages. This study used IT2FL to analyze Igbo emotion words. IT2F sets are computed using the interval approach method which is divided into two parts: the data part and the fuzzy set part. The data part preprocessed data and its statistics computed for the interval that survived the preprocessing stages while the fuzzy set part determined the nature of the footprint of uncertainty; the IT2F set mathematical models for each emotion characteristics of each emotion word is also computed. The data used in this work was collected from fifteen subjects who were asked to enter an interval for each of the emotion characteristics: Valence, Activation and Dominance on an interval survey of the thirty Igbo emotion words. With this, the words are being analyzed and can be used for the purposes of translation between vocabularies in consideration to context.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
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.
Unification Of Randomized Anomaly In Deception Detection Using Fuzzy Logic Un...IJORCS
In the recent era of computer electronic communication we are currently facing the critical impact of Deception which plays its vital role in the mode of affecting efficient information sharing system. Identifying Deception in any mode of communication is a tedious process without using the proper tool for detecting those vulnerabilities. This paper deals with the efficient tools of Deception detection in which combined application implementation is our main focus rather than with its individuality. We propose a research model which comprises Fuzzy logic, Uncertainty and Randomization. This paper deals with an experiment which implements the scenario of mixture application with its revealed results. We also discuss the combined approach rather than with its individual performance.
Master Industrial series UPS provide maximum protection and power quality for any type of load, especially industrial applications, such as manufacturing and petrochemical processes.
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
Several attempts had been made to analyze emotion words in the fields of linguistics, psychology and sociology; with the advent of computers, the analyses of these words have taken a different dimension. Unfortunately, limited attempts have so far been made to using interval type-2 fuzzy logic (IT2FL) to analyze these words in native languages. This study used IT2FL to analyze Igbo emotion words. IT2F sets are computed using the interval approach method which is divided into two parts: the data part and the fuzzy set part. The data part preprocessed data and its statistics computed for the interval that survived the preprocessing stages while the fuzzy set part determined the nature of the footprint of uncertainty; the IT2F set mathematical models for each emotion characteristics of each emotion word is also computed. The data used in this work was collected from fifteen subjects who were asked to enter an interval for each of the emotion characteristics: Valence, Activation and Dominance on an interval survey of the thirty Igbo emotion words. With this, the words are being analyzed and can be used for the purposes of translation between vocabularies in consideration to context.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
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.
Unification Of Randomized Anomaly In Deception Detection Using Fuzzy Logic Un...IJORCS
In the recent era of computer electronic communication we are currently facing the critical impact of Deception which plays its vital role in the mode of affecting efficient information sharing system. Identifying Deception in any mode of communication is a tedious process without using the proper tool for detecting those vulnerabilities. This paper deals with the efficient tools of Deception detection in which combined application implementation is our main focus rather than with its individuality. We propose a research model which comprises Fuzzy logic, Uncertainty and Randomization. This paper deals with an experiment which implements the scenario of mixture application with its revealed results. We also discuss the combined approach rather than with its individual performance.
Master Industrial series UPS provide maximum protection and power quality for any type of load, especially industrial applications, such as manufacturing and petrochemical processes.
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
Speech Emotion Recognition Using Neural Networksijtsrd
Speech is the most natural and easy method for people to communicate, and interpreting speech is one of the most sophisticated tasks that the human brain conducts. The goal of Speech Emotion Recognition SER is to identify human emotion from speech. This is due to the fact that tone and pitch of the voice frequently reflect underlying emotions. Librosa was used to analyse audio and music, sound file was used to read and write sampled sound file formats, and sklearn was used to create the model. The current study looked on the effectiveness of Convolutional Neural Networks CNN in recognising spoken emotions. The networks input characteristics are spectrograms of voice samples. Mel Frequency Cepstral Coefficients MFCC are used to extract characteristics from audio. Our own voice dataset is utilised to train and test our algorithms. The emotions of the speech happy, sad, angry, neutral, shocked, disgusted will be determined based on the evaluation. Anirban Chakraborty "Speech Emotion Recognition Using Neural Networks" 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/ijtsrd47958.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/47958/speech-emotion-recognition-using-neural-networks/anirban-chakraborty
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.
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%
Emotion Detection from Voice Based Classified Frame-Energy Signal Using K-Mea...ijseajournal
Emotion detection is a new research era in health informatics and forensic technology. Besides having some challenges, voice based emotion recognition is getting popular, as the situation where the facial image is not available, the voice is the only way to detect the emotional or psychiatric condition of a
person. However, the voice signal is so dynamic even in a short-time frame so that, a voice of the same person can differ within a very subtle period of time. Therefore, in this research basically two key criterion have been considered; firstly, this is clear that there is a necessity to partition the training data according
to the emotional stage of each individual speaker. Secondly, rather than using the entire voice signal, short time significant frames can be used, which would be enough to identify the emotional condition of the speaker. In this research, Cepstral Coefficient (CC) has been used as voice feature and a fixed valued kmeans clustered method has been used for feature classification. The value of k will depend on the number
of emotional situations in human physiology is being an evaluation. Consequently, the value of k does not necessarily consider the volume of experimental dataset. In this experiment, three emotional conditions: happy, angry and sad have been detected from eight female and seven male voice signals. This methodology has increased the emotion detection accuracy rate significantly comparing to some recent works and also reduced the CPU time of cluster formation and matching.
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
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
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
1. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
UNDERSTANDING AND ESTIMATION OF
EMOTIONAL EXPRESSION USING
ACOUSTIC ANALYSIS OF NATURAL SPEECH
Nilima Salankar Fulmare1 ,Prasun Chakrabarti2and Divakar Yadav3
1,2
Department of Computer Science, Sir Padampat Singhania University,Udaipur
3
IET,Lucknow
ABSTRACT
Emotion expression is an essential function for daily life that can be severely affected some psychological
disorders. In this paper we identified seven emotional states anger,surprise,sadness ,happiness,fear,disgust
and neutral.The definition of parameters is a crucial step in the development of a system for emotion
analysis.The
15
explored
features
are
energy
intensity,pitch,standard
deviation,jitter,shimmer,autocorrelation,noise to harmonic ration,harmonic to noise ration,energy entropy
block,short term energy,zero crossing rate,spectral roll-off,spectral centroid and spectral flux,and formants
In this work database used is SAVEE(Surrey audio visual expressed emotion).Results by using different
learning methods and estimation is done by using a confidence interval for identified parameters are
compared and explained.The overall experimental results reveals that Model 2 and Model 3 give better
results than Model 1 using learning methods and estimation shows that most emotions are correctly
estimated by using energy intensity and pitch.
KEYWORDS
Emotion,Confidence Interval, Speech, SAVEE database, Paralinguistic information
1. INTRODUCTION
Human speech is an acoustic waveform generated by the vocal apparatus, whose parameters are
modelled by the speaker to convey information. The physical characteristics and the mental state
of the speaker also determine how these parameters are affected and consequently how speech
conveys intended and on occasion unintended information.Knowledge about how these
parameters characterise the information is not explicitly available human brain is able to decipher
the information from resulting speech signal,including the emotional state of the
speaker.Information about emotional state is expressed via speech through numerous cues,ranging
from low level acoustic ones to high level linguistic content.Several approaches to speech based
automatic emotion recognition, each taking advantage of few of these cues have been
explored[1]-[9].The most commonly used acoustic and prosodic based on cepstral coefficients ,
pitch,intensity and speech rate. The research of automatic speech emotion recognition enhance the
efficiency of people’s work and study and its helpful for solving problems more efficiently.In the
present work,we report results on recogning emotional states from a corpus of short duration
spoken utterances. Specifically we aim at recognizing six emotional states
fear,happiness,sadness,surprise,disgust,anger and additional neutral. The rest of the paper is as
follows: In section II we describe the database that were used for an analysis. Section III
describes the feature extraction process and the composition of feature vectors. Subsequently in
DOI : 10.5121/ijnlc.2013.2503
37
2. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
section IV Experiments and Evaluations are presented .Section V describes conclusion and future
work.
2. WORKING DATABASE
Both extraction of features and the emotion identification experiments described in this paper
were carried using the SAVEE(Surrey audio visual expressed emotion) database[10].The
database consists of four actors of ages 27-31 depicting the widely used six basic emotions
(fear,anger,disgust,sadness,surprise,happiness) plus the neutral state.Recording consists of 15
phonetically balanced TIMIT sentences per emotion(with additional 30 sentences for neutral
state) resulting into corpus of 480 British English utternaces In this study we have analysed three
speakers.This database was chosen because it presents certain characteristics that were of interest.
3. FEATURE EXTRACTION
Performance of any emotion recognition strategy largely depends on how relevant features
,invariants to speaker ,language and contents could be extracted .Our approach considers a feature
vector which consists of 15 basic acoustic features.Features extracted are intensity,pitch,standard
deviation,jitter,shimmer,autocorrelation,noise to harmonic ration,harmonic to noise
ration,energy entropy block,short term energy,zero crossing rate,spectral roll-off,spectral
centroid and spectral flux,and formants.Previous research in the field of emotion recognition has
shown that emotional reactions are strongly related to pitch and energy of the spoken
message[11].For example the pitch of speech associated with anger or happiness is always higher
than that of associated with sadness or fear and the energy associated with anger is greater than
that associated with fear. For extraction of features software used is PRATT[12]. Some features
are extracted by using this software and for the rest features matlab has been used.
A. Composition of Features
Fifteen features are grouped in a different combination
classification accuracy and dependency of features.
for the precise understating of
1.Energy Intensity+Pitch
2.EnergyIntensity+Pitch+Standard Deviation+Jitter+Shimmer
4.All extracted features
4. EXPERIMENTS AND EVALUATION
Our
task
was
to
evaluate
the
performance
of
Neural
Network(NN),NaiveBayes(NB),Classification Tree(CT) and KNN algorithm for the identification
of seven emotional states.For these experiments we have used Orange Canvas[13].Experimental
setup used for analysis includes Neural Network with 20 hidden layers,1.0 regularization factor
and maximum iterations 300. Naïve Bayes with size of LOESS window 0.5.Classification Tree
with attribute selection criteria Information Gain. KNN with the metrices Euclidean .70%(252
utterances) of data has been used for training and 30% (108 utterances) data used for testing
purpose. Testing method used for evaluation is CV10. The evaluation based on classification
accuracy(CA),Sensitivity(SE),Specificity(SP),Brier Score(BS).An explanation of the features are
CA:Proportion of the correctly classified example.
BA:Average deviation between the predicted probabilities of events and actual events.
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3. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
Sensistivity and Specificity is as shown in table I.
TABLE I
SENSITIVITY AND SPECIFICITY
<70
Clinical
NonClinical
True positive
False positive
>=70
False
negative
True
negative
As we are measuring human behaviour and not a physical characteristics, there is always some
measurement error inherent in all clinical tests. Sometimes a test has cut-off score to determine if
the client is at risk and should be referred for more in-depth testing or has a particular disorder. So
in hypothetical test of language development any client with a score more than two standard
deviations below the mean(i.e. 70) will be classified as having a language disorder and any
subject whose scores above 70 will be classified as nonclinical[14].As shown in table II subjects
in the known clinical samples with score below 70 are considered true positive because they are
correctly classified as having disorder. This percentage represents an estimate of the sensistivity
of the test. Subjects in the nonclinical samples with score of 70 or higher are considered true
negatives as they are correctly classified as not having the disorder. This percentage represent an
estimate of the specificity of the test.
B. Results
In an experimentation 3 models have been designed
Model 1:Composed of attributes Energy Intensity and Pitch.
Results of this model are summarized in Table IV and Table V.Table IV summarize the CA,BS
and sensistivity and specificity for 7 identified classes.Table V summarize Confusion matrix for
the correct identified class.
Model 2: Composed of attributes Energy Intensity,Pitch,Standard Deviation,Jitter and Shimmer.
Results of this model are summarized in Table VI and Table VII.Table VI summarize the CA,BS
and sensistivity and specificity for 7 identified classes. Table VII summarize Confusion matrix for
the correct identified class.
Model 3: Composed of all extracted features mentioned in section III. Results of this model are
summarized in Table VIII and Table IX. Table VIII summarized the CA,BS and sensistivity and
specificity for 7 identified classes. Table IX summarize Confusion matrix for the correct
identified class.
C. Comparative Results
Among three designed models highest classification accuracy achieved for Model 2 as shown
in table II.
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4. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
TABLE II
CLASSIFICATION ACCURACY OF MODELS
Model
1
2
3
NN
70.80%
71.39%
59.85%
NB
68.33%
72.89%
49.00%
CT
61.53%
73.87%
53.80%
KNN
65.28%
74.39%
50.18%
As per Confusion Matrix highest accuracy achieved for the classification of seven emotions is as
shown in Table III.
TABLE III
EMOTION CLASSIFICATION ACCURACY OF MODELS
Emotion
Anger
Disgust
Fear
Happiness
Neutral
Sadness
Surprise
Model
1
2
2
2
1
1
1
Accuracy(%)
87.1
68.2
66.7
79.3
91.8
96.8
77.4
Classifier
NN
KNN
NN
CT
NN/NB
NN
NN
D. Summary of Results
Model 1 and Model 2 gives the highest result. Neutral Anger,Sadness and Surprise is best
classsfied by Model 1 and Disgust Fear and Happiness best classified by Model 2.
TABLE IV
ENERGY INTENSITY+PITCH
A:Anger D:Disgust F:Fear H:Happiness N:Neutral S:Sad U:Surprise
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5. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
TABLE V
CONFUSION MATRIX FOR ATTRIBUTES ENERGY INTENSITY+PITCH
TABLE VI
ENERGY INTENSITY+P ITCH+STANDARD DEVIATION+JITTER+SHIMMER
A:Anger D:Disgust F:Fear H:Happiness N:Neutral S:Sad U:Surprise
TABLE VII
CONFUSION MATRIX FOR ATTRIBUTES ENERGY INTENSITY+PITCH+STANDARD
DEVIATION+JITTER+SHIMMER
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6. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
TABLE VIII
ALL EXTRACTED FEATURES
A:Anger D:Disgust F:Fear H:Happiness N:Neutral S:Sad U:Surprise
TABLE IX
CONFUSION MATRIX FOR COMPLETE EXTRACTED FEATURES
We have done estimation of emotions on the basis of two features Energy Intensity and
Pitch.Graphs 1a and1b,shows that an confidence interval is more wider for emotion anger by
using pitch as an attribute as compare to energy intensity.Graphs 2a and 2b shows that
confidence interval is more wider for emotion disgust by using energy intensity attribute as
compare to pitch. Graphs 3a and 3b shows that confidence interval is more wider for emotion
fear by using pitch attribute as compare to Energy Intensity. Graphs 4a and 4b shows that
confidence interval is almost same for emotion happiness by using energy intensity and pitch.
Graphs 5a and 5b shows that estimation is not possible for an emotion sadness by using an
attribute energy intensity and pitch we need to provide some more information for its
estimation.Graphs 6a and 6b shows that confidence interval is more wider for emotion surprise
by using an attribute pitch as compare to energy intensity.
Graph 1 a (Anger)
Graph 1b(Anger)
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7. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
Graph 2 a
(Disgust)
Graph 3 a (Fear)
Graph 4 a (Happiness)
Graph 2b(Disgust)
Graph 3b(Fear)
Graph 4b(Happiness)
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8. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
Graph5 a (Sadness)
Graph6 a (Surprise)
Graph 5b(Sadness)
Graph 6b(Surprise)
5. CONCLUSION and FUTURE WORK
This paper presents a comparative study of different classifiers based on different combination
of features. Feature Extraction was performed by using PRATT software.This paper analyses
which combination of features provide better results.By using confidence interval estimation
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9. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
we can estimate most of the basic emotions with less wider range either by using an energy
intensity or pitch.
Future work includes expanding of the study based on larger extracted features and more
number of database. And also modelling the understanding of the classification based on
different feature combination by using an Event-B approach.
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10. International Journal on Natural Language Computing (IJNLC) Vol. 2, No.4, October 2013
Authors
Nilima Salankar Fulmare is serving as Assistant Professor Engineering of Sir Padampat
Singhania University ,Udaipur,India. She has done her B.E. in computer science and
engineering from Shri Guru Govind Singhji college of Science and
Technology.Mtech.Tech in Computer Engineering and pursuing PhD(Engg) in the area of
Human Computer Interaction
Dr.Prasun Chakrabarti (09/03/1981) is serving as Associate Professor and Head of the
department of Computer Science and Engineering of Sir Padampat Singhania University
,Udaipur,India. He has done his B.Tech in Computer Engineering in 2003 , M.E.
inComputer Science and Engineering in 2005 and PhD(Engg) in the area of Information
Security in 2009. Dr.Chakrabarti has filed 8 Indian Patents and has
about 113 publications in his credit. He visited Waseda University, Japan from 16th to 25th May 2012 as
Honorary Visiting Professor under INSA-CICS Travel Fellowship Award. He is a senior member of
IACSIT(Singapore) ; life-member of Indian Science Congress Association , Calcutta Mathematical
Society , Calcutta Statistical Association , Indian Society for Technical Education , Cryptology
Research Society of India , IAENG(Hong Kong), Computer Science Teachers’ Association(USA),
International Institute for Software Testing(USA) .He is Reviewer of International journal of Information
Processing and Management (Elsevier) , IEEE Transactions on Industrial Informatics , Journal of
Medical Systems , Springer , etc.
Divakar Singh Yadav,working as Professor in Department of Computer Science &
Engineering, Head, Department of Computer Science and Engineering, Head, Training and
Placement Cell, Institute of Engineering and Technology, Lucknow.
46