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Dissertations / Theses on the topic 'Emotion detection from speech'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Nallamilli, Sai Chandra Sekhar Reddy, and Nihanth Kandi. "Detection of Human Emotion from Noise Speech." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19610.

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Detection of a human emotion from human speech is always a challenging task. Factors like intonation, pitch, and loudness of signal vary from different human voice. So, it's important to know the exact pitch, intonation and loudness of a speech for making it a challenging task for detection. Some voices exhibit high background noise which will affect the amplitude or pitch of the signal. So, knowing the detailed properties of a speech to detect emotion is mandatory. Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been app

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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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3

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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4

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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5

Bandhakavi, Anil. "Domain-specific lexicon generation for emotion detection from text." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3103.

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Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to understand emotions from text are limited in effectiveness due to reliance on general purpose emotion lexicons that have static vocabulary and sentiment lexicons that can only interpret emotions coarsely. Thus emotion detection from text calls for methods and knowledge resources that can deal with challenges such as dynamic and informal vocabulary, doma

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Wejdelind, Marcus, and Nils Wägmark. "Multi-speaker Speech Activity Detection From Video." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297701.

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A conversational robot will in many cases have todeal with multi-party spoken interaction in which one or morepeople could be speaking simultaneously. To do this, the robotmust be able to identify the speakers in order to attend to them.Our project has approached this problem from a visual pointof view where a Convolutional Neural Network (CNN) wasimplemented and trained using video stream input containingone or more faces from an already existing data set (AVA-Speech). The goal for the network has then been to for eachface, and in each point in time, detect the probability of thatperson speak

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7

Loutrari, Ariadni Despoina. "Prosody beyond pitch and emotion in speech and music : evidence from right hemisphere brain damage and congenital amusia." Thesis, Birkbeck (University of London), 2016. http://bbktheses.da.ulcc.ac.uk/330/.

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This dissertation examines the relationship of prosodic processing in language and music from a new perspective, considering acoustic features that have not been studied before in the framework of the parallel study of language and music. These features are argued to contribute to the effect of ‘expressiveness’ which is here defined as the combination of the acoustic features (variation in duration, pitch, loudness, and articulation) that results in aesthetic appreciation of the linguistic and the musical acoustic stream and which is distinct from pitch, emotional and pragmatic prosody as well

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8

Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.

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9

Söderqvist, Filip. "Emotion Detection from Electroencephalography Data with Machine Learning : Classification of emotions elicited by auditory stimuli from music on self-collected data sets." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296703.

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The recent advances in deep learning have made it state-of-the-art for many different tasks, making its potential usefulness for analyzing electroencephalography (EEG) data appealing. This study aims at automatic feature extraction and classification of likeability, valence, and arousal elicited by auditory stimuli from music by training deep neural networks (DNNs) on minimally pre-processed multivariate EEG time series. Two data sets were collected, the first containing 840 samples from 21 subjects, the second containing 400 samples from a single subject. Each sample consists of a 30 second

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10

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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11

Halliday, Laura Alyson. "Emotion detection : can perceivers identify an emotion from limited information? : a thesis submitted in fulfilment of the requirements of the degree of Master of Arts in Psychology at the University of Canterbury /." Thesis, University of Canterbury. Psychology, 2008. http://hdl.handle.net/10092/1961.

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Perceivers who can accurately identify an emotion are more likely to engage in successful social interactions. Research has shown that perceivers can accurately identify emotions from facial expressions. However, in real life not all the features of a displayer’s face are always visible to the perceiver. The aim of this study was to discover if participants could accurately identify facial expressions of emotion from limited information. Two experiments were carried out to investigate this idea using static photographs of genuine and posed happy, sad and fearful facial expressions. Certain fea

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12

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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13

Kun-YiHuang and 黃琨義. "A Study on Emotion Recognition from Elicited Speech Responses for Mood Disorder Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/a2uss7.

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14

Chia-HuiChou and 周佳惠. "Mood Disorder Detection from Speech Using LSTM-Based Emotion Profile Tracking and Mood Verification." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/kp29q6.

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碩士<br>國立成功大學<br>資訊工程學系<br>104<br>In mental health disorder, Unipolar Depression (UD) and Bipolar Disorder (BD) have become the most common mental illness. A large portion of the BD patients is misdiagnosed as UD on initial presentation. As speech is the most natural way to express emotion, this thesis focus on tracking emotion profile of speech to build a short-term mood disorder detector for diagnosis assistance. This thesis proposes an approach to short-term detection of mood disorder based on the elicited speech responses. At first, eliciting emotional videos are used to elicit the patients

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15

Kao, Yu-Cheng, and 高鈺程. "Speech Emotion Baseline Model for Speaker Dependent Abnormal Emotion Detection." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/52489205221944576637.

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碩士<br>大同大學<br>資訊工程學系(所)<br>101<br>In daily life, the emotion is unavoidable in human interaction. To express emotion is quite easy, but it is difficult to recognize emotion of human by computer. In general, emotion state of a human can be recognized from facial expression, gesture, and voice. Speech emotion involves the meanings that we say and the way it is said. The speech emotion recognition has attracted quite a lot of attention in recent years. In this research, we studied the speaker dependent speech emotion recognition. The speech features and the classifiers are the major research topi

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16

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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17

Pai, Chen-yu, and 白鎮宇. "Analysis and Detection of Emotion Change in Continuous Speech." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/82343003498623163128.

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碩士<br>大同大學<br>資訊工程學系(所)<br>96<br>Speech communication plays an important role for human beings. Human speech is not only involving the syntax but also the feeling at the moment. In this thesis we use 11 kinds of speech features, including formant, shimmer, jitter, Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Mel-Frequency Cepstral Coefficients (MFCC), first derivative of MFCC (D-MFCC), second derivative of MFCC (DD-MFCC), Log Frequency Power Coefficients (LFPC), Perceptual Linear Prediction (PLP) and RelAtive SpecTrAl PLP (RastaPLP) as the features for

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18

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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19

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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20

Vogt, Thurid [Verfasser]. "Real-time automatic emotion recognition from speech / von Thurid Vogt." 2010. http://d-nb.info/1010495038/34.

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21

Liao, Duan-Li, and 廖惇利. "Development of a Hybrid Neural Network Model for Emotion Recognition from Speech." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/85966028619221057437.

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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>94<br>The application of the emotion recognition in the Human-Computer Interaction (HCI) is quite popular. To accurately recognize emotion is the goal of our investigation. In this thesis, we adopt the approach of model combination and purpose a novel hybrid model to improve the recognition rate. In our approach, we normalize the information of each single model and get a complementary result by combining those single models for improving the recognition rate. Finally, we obtain a more accurate recognition rate by the output of the hybrid model. We use artificial

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22

Hung, Yi-Ting, and 洪意婷. "A Study on the Recognition of Specified Emotion from Continuous Speech Signals." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/60605828866487180485.

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碩士<br>大同大學<br>資訊工程學系(所)<br>103<br>Speech emotion recognition is a process to recognize the emotion of the speaker from the uttered speech signal. To be more practical, it is necessary to use the natural dialogues as the training and testing corpus for continuous speech emotion recognition. Specific emotions recognitions from continuous speech can assist cell center systems, life-line and other telephone services. In order to reduce the computation time on practical applications and improve the efficiency, we reduce the number of features in the recognition process while maintain an acceptable

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23

Lu, Meng-Ju, and 呂孟儒. "Learning Thematic Role Assignment and Emotion Detection from Text Sentences Using Adaboost Algorithms." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/46207204694676848585.

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碩士<br>國立清華大學<br>資訊工程學系<br>98<br>Due to the difficulties of building emotion model for a dialogue agent directly from a dialogue sentence, we propose a two stage by mapping words in a dialogue text sentence into thematic their roles and then inferring the embedded emotion from the dialogue sentence based on the thematic information. Therefore, in this thesis, we focus on the learning classification problems: one is the thematic role assignment based on syntactic structure information and features of words of a sentence and the other the emotion detection based on thematic role information assig

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Yuan-RongZeng and 曾苑蓉. "Mood Detection using LSTM-based Fusion of Emotion Expressions from Asynchronous Multimodal Inputs." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7ag56k.

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25

Wang, Wei-Zheng, and 王惟正. "Automatic Detection of Pronunciation Error Patterns from Speech Signals in Mandarin Chinese." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/79954050784337087094.

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Huang, Tzu-Yun, and 黃姿云. "A Dual Complementary Acoustic Embedding Network: Mining Discriminative Characteristics from Raw-waveform for Speech Emotion Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y8zcm7.

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27

Graziani, Lisa. "Constrained Affective Computing." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238365.

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Emotions have an important role in daily life, influence decision-making, human interaction, perception, attention, self-regulation. They have been studied since ancient times, philosophers have been always interested in analyzing human nature and bodily sensations, psychologists in studying the physical and psychological changes that influence thought and behavior. In the early 1970s, the psychologist Paul Ekman defined six universal emotions, namely anger, disgust, fear, happiness, sadness, and surprise. This categorization has been taken into account for several studies. In the late 1990s,

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