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Dissertations / Theses on the topic 'Speech emotion recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sidorova, Julia. "Optimization techniques for speech emotion recognition." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7575.

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Hay tres aspectos innovadores. Primero, un algoritmo novedoso para calcular el contenido emocional de un enunciado, con un diseño mixto que emplea aprendizaje estadístico e información sintáctica. Segundo, una extensión para selección de rasgos que permite adaptar los pesos y así aumentar la flexibilidad del sistema. Tercero, una propuesta para incorporar rasgos de alto nivel al sistema. Dichos rasgos, combinados con los rasgos de bajo nivel, permiten mejorar el rendimiento del sistema.<br>The first contribution of this thesis is a speech emotion recognition system called the ESEDA capable of

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2

Pachoud, Samuel. "Audio-visual speech and emotion recognition." Thesis, Queen Mary, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528923.

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3

Iliev, Alexander Iliev. "Emotion Recognition Using Glottal and Prosodic Features." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_dissertations/515.

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Emotion conveys the psychological state of a person. It is expressed by a variety of physiological changes, such as changes in blood pressure, heart beat rate, degree of sweating, and can be manifested in shaking, changes in skin coloration, facial expression, and the acoustics of speech. This research focuses on the recognition of emotion conveyed in speech. There were three main objectives of this study. One was to examine the role played by the glottal source signal in the expression of emotional speech. The second was to investigate whether it can provide improved robustness in real-world

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4

Väyrynen, E. (Eero). "Emotion recognition from speech using prosodic features." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526204048.

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Abstract Emotion recognition, a key step of affective computing, is the process of decoding an embedded emotional message from human communication signals, e.g. visual, audio, and/or other physiological cues. It is well-known that speech is the main channel for human communication and thus vital in the signalling of emotion and semantic cues for the correct interpretation of contexts. In the verbal channel, the emotional content is largely conveyed as constant paralinguistic information signals, from which prosody is the most important component. The lack of evaluation of affect and emotional

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5

Ma, Rui. "Parametric Speech Emotion Recognition Using Neural Network." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-17694.

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The aim of this thesis work is to investigate the algorithm of speech emotion recognition using MATLAB. Firstly, five most commonly used features are selected and extracted from speech signal. After this, statistical values such as mean, variance will be derived from the features. These data along with their related emotion target will be fed to MATLAB neural network tool to train and test to make up the classifier. The overall system provides a reliable performance, classifying correctly more than 82% speech samples after properly training.

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6

Sadok, Samir. "Audiovisual speech representation learning applied to emotion recognition." Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0003.

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Les émotions sont vitales dans notre quotidien, devenant un centre d'intérêt majeur de la recherche en cours. La reconnaissance automatique des émotions a suscité beaucoup d'attention en raison de ses applications étendues dans des secteurs tels que la santé, l'éducation, le divertissement et le marketing. Ce progrès dans la reconnaissance émotionnelle est essentiel pour favoriser le développement de l'intelligence artificielle centrée sur l'humain. Les systèmes de reconnaissance des émotions supervisés se sont considérablement améliorés par rapport aux approches traditionnelles d’apprentissag

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7

Rintala, Jonathan. "Speech Emotion Recognition from Raw Audio using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278858.

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Traditionally, in Speech Emotion Recognition, models require a large number of manually engineered features and intermediate representations such as spectrograms for training. However, to hand-engineer such features often requires both expert domain knowledge and resources. Recently, with the emerging paradigm of deep-learning, end-to-end models that extract features themselves and learn from the raw speech signal directly have been explored. A previous approach has been to combine multiple parallel CNNs with different filter lengths to extract multiple temporal features from the audio signal,

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8

Mancini, Eleonora. "Disruptive Situations Detection on Public Transports through Speech Emotion Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24721/.

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In this thesis, we describe a study on the application of Machine Learning and Deep Learning methods for Voice Activity Detection (VAD) and Speech Emotion Recognition (SER). The study is in the context of a European project whose objective is to detect disruptive situations in public transports. To this end, we developed an architecture, implemented a prototype and ran validation tests on a variety of options. The architecture consists of several modules. The denoising module was realized through the use of a filter and the VAD module through an open-source toolkit, while the SER system was

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9

Al-Talabani, Abdulbasit. "Automatic Speech Emotion Recognition : feature space dimensionality and classification challenges." Thesis, University of Buckingham, 2015. http://bear.buckingham.ac.uk/101/.

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In the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SE

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10

Sun, Rui. "The evaluation of the stability of acoustic features in affective conveyance across multiple emotional databases." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49041.

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The objective of the research presented in this thesis was to systematically investigate the computational structure for cross-database emotion recognition. The research consisted of evaluating the stability of acoustic features, particularly the glottal and Teager Energy based features, and investigating three normalization methods and two data fusion techniques. One of the challenges of cross-database training and testing is accounting for the potential variation in the types of emotions expressed as well as the recording conditions. In an attempt to alleviate the impact of these types of va

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11

Noé, Paul-Gauthier. "Emotion Recognition in Football Commentator Speech : Is the action intense or not ?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289370.

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In order to improve the production quality of a football game broadcast, Digigram wants to detect automatically the excitement state of the commentator. The aim of this master thesis is to obtain this state from the commentator speech in order to know if s/he is describing an intense action or a calm one. In order to do that, a simple binary classification problem is defined. A speech segment has to be classified as being either from an intense action or a calm one. The audio waveform is not directly used for classification. Relevant features are used instead, such as the Mel-Frequency Cepstra

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12

Tinnemore, Anna, and Anna Tinnemore. "Improving Understanding of Emotional Speech Acoustic Content." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625368.

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Children with cochlear implants show deficits in identifying emotional intent of utterances without facial or body language cues. A known limitation to cochlear implants is the inability to accurately portray the fundamental frequency contour of speech which carries the majority of information needed to identify emotional intent. Without reliable access to the fundamental frequency, other methods of identifying vocal emotion, if identifiable, could be used to guide therapies for training children with cochlear implants to better identify vocal emotion. The current study analyzed recordings of

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13

Bhullar, Naureen. "Effects of Facial and Vocal Emotion on Word Recognition in 11-to-13-month-old infants." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/27502.

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The speech commonly addressed to infants (infant-directed speech or IDS) is believed to have multiple functions, including communication of emotion and highlighting linguistic aspects of speech. However, these two functions are most often studied separately so that the influence of emotional prosody (the changes in intonation and vocal quality that relate to emotion) on linguistic processing in infants has rarely been addressed. Given that language learning during infancy occurs in the context of natural infant-caretaker exchanges that most certainly include emotion communication and co-regul

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Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.

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15

Siddiqui, Mohammad Faridul Haque. "A Multi-modal Emotion Recognition Framework Through The Fusion Of Speech With Visible And Infrared Images." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556459232937498.

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16

Acosta, Jaime Cesar. "Using emotion to gain rapport in a spoken dialog system." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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17

Pon-Barry, Heather Roberta. "Inferring Speaker Affect in Spoken Natural Language Communication." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10710.

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The ﬁeld of spoken language processing is concerned with creating computer programs that can understand human speech and produce human-like speech. Regarding the problem of understanding human speech, there is currently growing interest in moving beyond speech recognition (the task of transcribing the words in an audio stream) and towards machine listening—interpreting the full spectrum of information in an audio stream. One part of machine listening, the problem that this thesis focuses on, is the task of using information in the speech signal to infer a person’s emotional or mental state. In

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18

Deng, Jun [Verfasser], Björn W. [Akademischer Betreuer] [Gutachter] Schuller, and Werner [Gutachter] Hemmert. "Feature Transfer Learning for Speech Emotion Recognition / Jun Deng. Betreuer: Björn W. Schuller. Gutachter: Björn W. Schuller ; Werner Hemmert." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1106382331/34.

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Iriya, Rafael. "Análise de sinais de voz para reconhecimento de emoções." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-14042015-160249/.

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Esta pesquisa é motivada pela crescente importância do reconhecimento automático de emoções, em especial através de sinais de voz, e suas aplicações em sistemas para interação homem-máquina. Neste contexto, são estudadas as emoções Felicidade, Medo, Nojo, Raiva, Tédio e Tristeza, além do estado Neutro, que são emoções geralmente consideradas como essenciais para um conjunto básico de emoções. São investigadas diversas questões relacionadas à análise de voz para reconhecimento de emoções, explorando vários parâmetros do sinal de voz, como por exemplo frequência fundamental (pitch), energia de c

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20

Chandrapati, Srivardhan. "Multi-modal expression recognition." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/762.

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21

Deschamps-Berger, Théo. "Social Emotion Recognition with multimodal deep learning architecture in emergency call centers." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG036.

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Cette thèse porte sur les systèmes de reconnaissance automatique des émotions dans la parole, dans un contexte d'urgence médicale. Elle aborde certains des défis rencontrés lors de l'étude des émotions dans les interactions sociales et est ancrée dans les théories modernes des émotions, en particulier celles de Lisa Feldman Barrett sur la construction des émotions. En effet, la manifestation des émotions spontanées dans les interactions humaines est complexe et souvent caractérisée par des nuances, des mélanges et étroitement liée au contexte. Cette étude est fondée sur le corpus CEMO, composé

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22

KHALIFA, INTISSAR. "Deep psychology recognition based on automatic analysis of non-verbal behaviors." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314920.

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Un aspetto estremamente cruciale nel dominio dell’interazione uomo-uomo è la comunicazione delle emozioni. Essere in grado di dedurre gli stati emotivi attraverso comportamenti non-verbali consente agli esseri umani di comprendere e ragionare su obiettivi ed intenti altrui. L’Affective Computing è una branca dell’informatica che mira a trarre vantaggio dal potere delle emozioni per facilitare un’interazione uomo-macchina più efficiente. L’obiettivo è dare alle macchine la capacità di esprimere, riconoscere e regolare le emozioni. In questa tesi, esamineremo in dettaglio il ruolo delle espressi

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23

Guerrero, Razuri Javier Francisco. "Decisional-Emotional Support System for a Synthetic Agent : Influence of Emotions in Decision-Making Toward the Participation of Automata in Society." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122084.

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Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans

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Žukas, Gediminas. "Kalbos emocijų požymių tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140617_133242-89394.

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Magistro baigiamajame darbe išnagrinėtas automatinio šnekos emocijų atpažinimo uždavinys. Nors pastaruoju metu šios srities populiarumas yra smarkiai išaugęs, tačiau vis dar trūksta literatūros aprašančios konkrečių požymių ar požymių rinkinių efektyvumą kalbos emocijoms atpažinti. Ši problema suformavo magistro baigiamojo darbo tikslą – ištirti akustinių požymių taikymą šnekos emocijoms atpažinti. Darbo metu buvo atlikta požymių sistemų analizė, sukurta emocijų požymių sistemų (rinkinių) testavimo sistema, kuria atliktas požymių rinkinių tyrimas. Tyrimo metu gauti rezultatai yra labai panašūs

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Vlasenko, Andrej. "Studentų emocinės būklės testavimo metu tyrimas panauduojant biometrines technologijas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120329_153219-37955.

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Disertacijoje nagrinėjamas kompiuterinės sistemos kūrimas, su kuria būtų galima nustatyti asmens psichoemicinę būseną pagal jo balso signalų požymius. Taip pat pateikiama vyzdžio skersmens matavimo sistema. Taigi, pagrindiniai mokslinio tyrimo objektai yra žmogaus balso požymiai ir jo vyzdžio dydžio pa-sikeitimo dinamika. Pagrindinis disertacijos tikslas – sukurti metodikas ir algo-ritmus, skirtus automatiškai apdoroti ir išanalizuoti balso signalo požymius. Šių sukurtų algoritmų taikymo sritis – streso valdymo sistemos programinė įranga. Šiame darbe sprendžiami keli pagrindiniai uždaviniai: a

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Zhu, Winstead Xingran. "Hotspot Detection for Automatic Podcast Trailer Generation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444887.

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With podcasts being a fast growing audio-only form of media, an effective way of promoting different podcast shows becomes more and more vital to all the stakeholders concerned, including the podcast creators, the podcast streaming platforms, and the podcast listeners. This thesis investigates the relatively little studied topic of automatic podcast trailer generation, with the purpose of en- hancing the overall visibility and publicity of different podcast contents and gen- erating more user engagement in podcast listening. This thesis takes a hotspot- based approach, by specifically defining

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27

Navrátil, Michal. "Rozpoznávání emočních stavů pomocí analýzy řečového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217263.

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The diploma thesis deals with the analysis of human emotional states speaker by the help of analyse speech signals. The thesis has two parts. In the first part, the process of speech generating is described in addition to the description of the commonly used pre-processing methods such as denoising or preemphasis. The first part also deals with the major and minor prosody features, these features are: the fundamental frequency, energy, spectral features and time domain features such as the speech rate. The second part of this thesis deals with a task of emotion recognition from the speech sign

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Pfeifer, Leon. "Automatické rozpoznávání emočních stavů člověka na základě analýzy řečového projevu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217520.

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The diploma thesis deals with the analysis of human emotional states. The thesis consists of three parts. The first part is charcterize, the process of speech generating, from phonetic and psychological poin of view. In the second part there are proccesed metods and contextual things.(preprocessing of signal, voice activity detector). For calculation fundamental Frequency it was used metod of central clipping, another used metod is formant frequency analyse and the last is metod of determinatin of nuber of thorns and planes. In the thirt part there are proccesesed results of measurements perfo

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Shaukat, Arslan. "Automatic Emotional State Analysis and Recognition from Speech Signals." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511910.

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Atassi, Hicham. "Rozpoznání emočního stavu z hrané a spontánní řeči." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-233665.

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Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Dru

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Ferro, Adelino Rafael Mendes. "Speech emotion recognition through statistical classification." Master's thesis, 2017. http://hdl.handle.net/10400.14/22817.

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O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional simulado Português, intitulada European Portuguese Emotional Discourse Database (EPEDD) e foram operados algoritmos de classificação estatística nessa base de dados. EPEDD é uma base de dados simulada, caracterizada por pequenos discursos (5 frases longas, 5 frases curtas e duas palavras), todos eles pronunciados por 8 atores—ambos os sexos igualmente representados—em 9 diferentes emoções (raiva, alegria, nojo, excitação, apatia, medo, surpre

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"Optimization techniques for speech emotion recognition." Universitat Pompeu Fabra, 2009. http://www.tesisenxarxa.net/TDX-0113110-133822/.

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Yeh, Jun-Heng, and 葉俊亨. "Emotion Recognition from Mandarin Speech Signals." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/2f4evr.

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碩士<br>大同大學<br>資訊工程學系(所)<br>92<br>In this thesis, a Mandarin speech based emotion classification method is presented. Five archetypal human emotions including anger, boredom, happiness, neutral and sadness are investigated. In emotion classification of speech signals, the conventional features are statistics of fundamental frequency, loudness, duration and voice quality. However, the performance of systems employing these features degrades substantially when more than two valence emotion categories are to be classified. For speech emotion recognition, we select 16 LPC coefficients, 12 LPCC coef

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Chiou, Bo-Chang, and 邱柏菖. "Cross-Lingual Automatic Speech Emotion Recognition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/23736438894309347506.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>102<br>In this paper, we propose a speech emotion recognition system which adopt acoustic feature with support vector machine. Our research evaluates on the well-known Berlin Database of Emotion Speech(EMO-DB). The baseline of EMO-DB is 85.2% accuracy. In our feature reducing research, we succeed to reduce feature set from 6552 features to 37 features and kept above 80% accuracy by reducing dynamic features, feature groups, functionals, and principal component analysis. We begin with the construction of a Mandarin, Taiwanese, and Hakka database of emotional speech,

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SHEN, MENG-JHEN, and 沈孟蓁. "Research on Speech Emotion Recognition Systems." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j5m53v.

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碩士<br>國立臺北科技大學<br>電機工程系<br>107<br>There are many people who are not good at expressing emotions, or sometimes they are just unwilling to express their emotions because of their position. These long-term accumulated negative emotions are likely to cause mental illness. For patients with mental illness, the detection of negative emotional states is more important. To detect the emotional state of a specific user for a long time, it is suitable to establish a high-accuracy identification model. The common negative emotions are nothing more than anger and sadness. Therefore, our research will dete

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CHENG, KUAN-JUNG, and 程冠融. "Cross-Lingual Speech Emotion Recognition Based on Speech Recognition Technology in An Emotional Speech Database in Mandarin, Taiwanese, and Hakka." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6c4m2x.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>107<br>With the development of artificial intelligence, machine learning and deep learning, there are considerable breakthroughs in recognition techniques such as image recognition and speech recognition. Especially in the speech recognition technology, whether it is everyone's smart phone, or the current popular smart speakers, these products are equipped with voice assistants to provide a convenient voice interactive interface. Allowing machines to understand human emotions helps to increase interaction with machines, so speech emotion recognition is an important i

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Wang, Chun-Ming, and 王俊明. "Speech Emotion Recognition using 2D texture features." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/6y77cs.

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碩士<br>中山醫學大學<br>應用資訊科學學系碩士班<br>101<br>Now, many people have series work stress, it will result in negative emotion. Therefore, this paper proposes Speech Emotion Recognition using 2D texture features. First, we use spectrogram technology to transform 1D signal into 2D signal. Next, we use cubic curve contrast enhancement method to enhance the 2D signal contrast. Final, the image texture extraction method will be used to extract the texture features of the 2D signal. In the experiment result, we use three emotional databases to test our proposed system and use artificial neural network to be ou

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Su, Yu-Che, and 蘇于哲. "Emotion Recognition based on Chinese Speech Signals." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/54453655022687274699.

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碩士<br>中華大學<br>電機工程學系(所)<br>96<br>In the relationship, how to express emotion correctly is an important aspect in communication. In this thesis, a speech based emotion classification method is presented. Five basic for human emotions including anger, boredom, happiness, neutral and sadness are investigated. In emotion classification of speech signals, the tradition features are statistics of pitch, pitch energy and duration. However, the performance of systems employing these features degrades substantially because these prosodic features are easily influenced by noise. For speech emotion recog

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Li, Pei-jia, and 李珮嘉. "Emotion Recognition from Continuous Mandarin Speech Signal." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/79746323884839442339.

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碩士<br>大同大學<br>資訊工程學系(所)<br>94<br>Language is a mean of communication and speech plays an important role in the society. So how to express emotion correctly is an important aspect in communication. In this thesis, emotion recognition from continuous Mandarin speech signal is implemented. In the experiment, Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are selected as the features used in the recognition. Five emotions are investigated, including anger, happiness, sadness, boredom, and neutral. Endpoint detection is tried to segment the continuous

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Bakhshi, Ali. "Speech emotion recognition using deep neural networks." Thesis, 2021. http://hdl.handle.net/1959.13/1430839.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Emotion recognition is an interdisciplinary research area in psychology, social science, signal processing, and image processing. From a machine learning point of view, emotion recognition is a challenging task due to the different modalities used to express emotions. In this Ph.D. thesis, various speech emotion recognition frameworks have been proposed, most of which have been designed based on deep neural networks using end-to-end learning. A combination of speech and physiological signals has been used in a multimodal model to recognise rea

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KOHLI, VIVEK KUMAR. "SPEECH AND PATTERN RECOGNITION FOR EMOTION CLASSIFICATION." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19112.

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Human speech itself is a very special feature that is used for communication and expression of feelings. Speech analysis is an interesting and developing field for researchers. Physiologists and scholars from around the world are experimenting with speech as a marker for the detection of human mental physiognomies and diseases. Through speech analysis we can identify different human emotions and depressions. In our work we build a speech emotion detection system using convolutional neural network (CNN). Mel-Frequency Cepstral Co-efficient (MFCC) was used for feature extraction an

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Roshan, Akash. "Emotion recognition and text-to-speech synthesis." Thesis, 2017. http://ethesis.nitrkl.ac.in/8887/1/2017_MT_ARoshan.pdf.

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Emotion recognition is generally done by analyzing one of the three things voice, face or body language. Our main objective in this thesis would be to find the emotional state of a person, entirely from his speech. So we develop a system which would first record a person’s voice and analyze it to determine the person’s emotion. There would be no other input to the system. In speech emotion recognition, extraction of speech features plays an important role. The basic speech features are pitch, rate of speech and energy, and these value can be extracted directly from acoustic waveform or speech

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Yeh, Lan-Ying, and 葉藍霙. "Spectro-Temporal Modulations for Robust Speech Emotion Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/56883607879338166423.

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碩士<br>國立交通大學<br>電信工程研究所<br>98<br>Speech emotion recognition is mostly considered in clean speech. In this thesis, joint Rate-Scale features (RS features) are extracted from an auditory model and are applied to detect the emotion status of noisy speech. The noisy speech is derived from the Berlin Emotional Speech database and the FAU AIBO database with added white and babble noises under various SNR levels. The clean train/noisy test scenario is investigated to simulate conditions with unknown noisy sources. The sequential forward floating selection (SFFS) method is adopted to demonstrate the r

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Wu, Chien-Feng, and 吳鑑峰. "Bimodal Emotion Recognition from Speech and Facial Expression." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/g8tuye.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>90<br>With the trend of computer technology, computers have come into human’s daily life gradually. For this reason, human machine interface with intelligence and humanity become an important research issue. Human emotion recognition is one of the critical topics. Recent research on emotion recognition includes the construction of a single emotion recognizer using speech or facial expression, and mixture bimodal architecture. The recognition models include hidden Markov model, support vector machine and artificial rule-based, etc. Among these approaches, the main

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"Emotion Recognition and Traumatic Brain Injury." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9087.

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abstract: Emotion recognition through facial expression plays a critical role in communication. Review of studies investigating individuals with traumatic brain injury (TBI) and emotion recognition indicates significantly poorer performance compared to controls. The purpose of the study was to determine the effects of different media presentation on emotion recognition in individuals with TBI, and if results differ depending on severity of TBI. Adults with and without TBI participated in the study and were assessed using the The Awareness of Social Inferences Test: Emotion Evaluation Test (TAS

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Huang, Ching-Hsiu, and 黃慶修. "Emotion recognition of spontaneous speech using mutiple-instance learning." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21389130030838831132.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>102<br>Because of the popularizing of smart carrying equipment, the chance of people to approach agents is increasing, and the issue of given agent emotion emerge from the enormous volume of increasing demand. In this research, we choose the the recognition of emotion in speech. For simplify the problem of recognition emotion, we ignore the content of speech, and simply recognizing emotion from the tone of speech. we choose the presentation environment as the research target to collect speech sound, and focus on the recognition of levels of nervous emotion. At th

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Hsu, Jin-Huai, and 許晉懷. "Bimodal Emotion Recognition System Using Image and Speech Information." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75013414815783421468.

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Chen, Chia-ying, and 陳嘉穎. "Speech Emotion Recognition Using Factor Analysis and Identity Vectors." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/14016869692695939636.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>104<br>In this paper, we challenge INTERSPEECH 2009 Emotion open performance sub-Challenge of 5 class problem. Our research evaluates on the well-known FAU Aibo database. We use OpenSMILE toolkit to extract low-level descriptors and compute the delta coefficients. Gaussian Mixture Model (GMM) is popular approach in speaker identification and speaker verification, we use GMM systems to speech emotion recognition. It contains four systems, the first one is simple GMM system. The second one is GMM-UBM system, it resolve the insufficiency of training data. The third is

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Yang, Bo-Cheng, and 楊博丞. "Adversarial Feature Augmentation for Cross-corpus Speech Emotion Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hrvxvp.

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碩士<br>國立臺灣科技大學<br>自動化及控制研究所<br>107<br>Cross-corpus speech emotion recognition (SER) is a difficult task since most of the state-of-the-art methods can only do well in a single corpus but not crossing two or more corpora. Therefore, cross-corpus SER started to considered as an issue and receive some attention. However, most of the methods for cross-corpus focus on reducing the difference betweencorpora, where they don’t employ the state-of-the-art methods for SER. Nowadays in unsupervised domain adaptation, where the goal is to train classifier powerful enough for target samples by a labeled so

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Manamela, Phuti John. "The automatic recognition of emotions in speech." Thesis, 2020. http://hdl.handle.net/10386/3347.

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Thesis(M.Sc.(Computer Science)) -- University of Limpopo, 2020<br>Speech emotion recognition (SER) refers to a technology that enables machines to detect and recognise human emotions from spoken phrases. In the literature, numerous attempts have been made to develop systems that can recognise human emotions from their voice, however, not much work has been done in the context of South African indigenous languages. The aim of this study was to develop an SER system that can classify and recognise six basic human emotions (i.e., sadness, fear, anger, disgust, happiness, and neutral) from speech

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