Relevant bibliographies by topics / Speech emotion recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Speech emotion recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Speech emotion recognition"

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A, Prof Swethashree. "Speech Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2637–40. http://dx.doi.org/10.22214/ijraset.2021.37375.

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Abstract: Speech Emotion Recognition, abbreviated as SER, the act of trying to identify a person's feelings and relationships. Affected situations from speech. This is because the truth often reflects the basic feelings of tone and tone of voice. Emotional awareness is a fast-growing field of research in recent years. Unlike humans, machines do not have the power to comprehend and express emotions. But human communication with the computer can be improved by using automatic sensory recognition, accordingly reducing the need for human intervention. In this project, basic emotions such as peace,
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Venkateswarlu, Dr S. China. "Speech Emotion Recognition using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48705.

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Abstract -- Speech signals are being considered as most effective means of communication between human beings. Many researchers have found different methods or systems to identify emotions from speech signals. Here, the various features of speech are used to classify emotions. Features like pitch, tone, intensity are essential for classification. Large number of the datasets are available for speech emotion recognition. Firstly, the extraction of features from speech emotion is carried out and then another important part is classification of emotions based upon speech. Hence, different classif
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Alexander, Jessica M., and Fernando Llanos. "High-arousal emotional speech enhances speech intelligibility and emotion recognition in noise." Journal of the Acoustical Society of America 157, no. 6 (2025): 4085–96. https://doi.org/10.1121/10.0036812.

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Prosodic and voice quality modulations of the speech signal offer acoustic cues to the emotional state of the speaker. In quiet, listeners are highly adept at identifying not only a speaker's words but also the underlying emotional context. Given that distinct vocal emotions possess varying acoustic characteristics, background noise level may differentially impact speech recognition, emotion recognition, or their interaction. To investigate this question, we assessed the effects of three emotional speech styles (angry, happy, neutral) on speech intelligibility and emotion recognition across fo
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Werner, S., and G. N. Petrenko. "Speech Emotion Recognition: Humans vs Machines." Discourse 5, no. 5 (2019): 136–52. http://dx.doi.org/10.32603/2412-8562-2019-5-5-136-152.

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Introduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources. The typical “big six” classification commonly used in technical applications
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S, Abhimanue, and Dr Jyothish K. John. "Survey on Speech Emotion Recognition with Expressive Speech Synthesis." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02527.

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Emotion plays a key role in identifying the state of a person, that is, whether they are angry, sad, happy, etc. The paper presents an integrated framework that recognizes emotions from speech, generates emotionally aware responses, and synchronizes facial expressions to provide an animated video response. The system provides real-time, empathetic interactions for emotional support. It focuses on identifying the emotion of the person, especially to know if the person is depressed or having a hard time, so that it can provide emotional support to them, to overcome the feeling of distress and is
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Tank, Vishal P., and S. K. Hadia. "Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4752. http://dx.doi.org/10.11591/ijece.v10i5.pp4752-4758.

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To obse
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Vishal, P. Tank, and K. Hadia S. "Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4752–58. https://doi.org/10.11591/ijece.v10i5.pp4752-4758.

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. T
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Huang, Chengwei, Guoming Chen, Hua Yu, Yongqiang Bao, and Li Zhao. "Speech Emotion Recognition under White Noise." Archives of Acoustics 38, no. 4 (2013): 457–63. http://dx.doi.org/10.2478/aoa-2013-0054.

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Abstract Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture
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Reddy, Dr N. V. Rajasekhar. "Speech Emotion Recognition Using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 30–36. http://dx.doi.org/10.22214/ijraset.2024.63859.

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Abstract: Speech is a powerful way to express our thoughts and feelings. It can give us valuable insights into human emotions. Speech emotion recognition (SER) is a crucial tool used in various fields like human-computer interaction (HCI), medical diagnosis, and lie detection. However, understanding emotions from speech is challenging. This research aims to address this challenge. It uses multiple datasets, including CREMA-D, RAVDESS, TESS, and SAVEE, to identify different emotional states. The researchers reviewed existing literature to inform their methodology. They used spectrograms and mel
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Morgan, Shae D. "Comparing Emotion Recognition and Word Recognition in Background Noise." Journal of Speech, Language, and Hearing Research 64, no. 5 (2021): 1758–72. http://dx.doi.org/10.1044/2021_jslhr-20-00153.

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Purpose Word recognition in quiet and in background noise has been thoroughly investigated in previous research to establish segmental speech recognition performance as a function of stimulus characteristics (e.g., audibility). Similar methods to investigate recognition performance for suprasegmental information (e.g., acoustic cues used to make judgments of talker age, sex, or emotional state) have not been performed. In this work, we directly compared emotion and word recognition performance in different levels of background noise to identify psychoacoustic properties of emotion recognition
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Dissertations / Theses on the topic "Speech emotion recognition"

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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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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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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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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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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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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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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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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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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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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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Books on the topic "Speech emotion recognition"

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. Emotion Recognition using Speech Features. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5143-3.

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Mary, Leena. Extraction of Prosody for Automatic Speaker, Language, Emotion and Speech Recognition. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91171-7.

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Rao, K. Sreenivasa. Robust Emotion Recognition using Spectral and Prosodic Features. Springer New York, 2013.

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. Emotion Recognition Using Speech Features. Springer London, Limited, 2012.

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. Emotion Recognition Using Speech Features. Springer New York, 2012.

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Anne, Koteswara Rao, Swarna Kuchibhotla, and Hima Deepthi Vankayalapati. Acoustic Modeling for Emotion Recognition. Springer, 2015.

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Anne, Koteswara Rao, Swarna Kuchibhotla, and Hima Deepthi Vankayalapati. Acoustic Modeling for Emotion Recognition. Springer, 2015.

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Introduction to EEG- and Speech-Based Emotion Recognition. Elsevier Science & Technology Books, 2016.

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Abhang, Priyanka A., Suresh C. Mehrotra, and Bharti Gawali. Introduction to EEG- and Speech-Based Emotion Recognition. Elsevier Science & Technology Books, 2016.

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Introduction to EEG- and Speech-Based Emotion Recognition. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-01959-1.

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Book chapters on the topic "Speech emotion recognition"

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Weninger, Felix, Martin Wöllmer, and Björn Schuller. "Emotion Recognition in Naturalistic Speech and Language-A Survey." In Emotion Recognition. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118910566.ch10.

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Cen, Ling, Zhu Liang Yu, and Wee Ser. "Maximum a Posteriori Based Fusion Method for Speech Emotion Recognition." In Emotion Recognition. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118910566.ch9.

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Wendemuth, Andreas, Bogdan Vlasenko, Ingo Siegert, Ronald Böck, Friedhelm Schwenker, and Günther Palm. "Emotion Recognition from Speech." In Cognitive Technologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43665-4_20.

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Farouk, Mohamed Hesham. "Emotion Recognition from Speech." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02732-6_7.

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Farouk, Mohamed Hesham. "Emotion Recognition from Speech." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69002-5_9.

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Sethu, Vidhyasaharan, Julien Epps, and Eliathamby Ambikairajah. "Speech Based Emotion Recognition." In Speech and Audio Processing for Coding, Enhancement and Recognition. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1456-2_7.

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Shashank, Valandas Sai, Nuthanakanti Bhaskar, K. Srujan Raju, and A. Raji Reddy. "Emotion Recognition Through Speech." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9442-7_69.

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Sacheth, N. S., and R. Jayashree. "Emotion Recognition of Speech." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27524-1_34.

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Johar, Swati. "Emotional Speech Recognition." In Emotion, Affect and Personality in Speech. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28047-9_5.

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Han, RongQi, Xin Liu, and Hui Zhang. "AESR: Speech Recognition With Speech Emotion Recogniting Learning." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-1045-7_8.

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Conference papers on the topic "Speech emotion recognition"

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Mohammed, Parvez, Bakir Hadzic, Mohammed Eyad Alkostantini, Naoyuki Kubota, Youssef Shiban, and Matthias Rätsch. "Hearing emotions: fine-tuning speech emotion recognition models." In 2024 The 5th Symposium on Pattern Recognition and Applications, edited by Xiaodan Pang. SPIE, 2025. https://doi.org/10.1117/12.3057659.

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Kumawat, Pooja, and Aurobinda Routray. "Improving Speech Emotion Recognition with Emotion Dynamics." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905158.

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Raisi, Kanza, and Asmaa Mourhir. "Real-Time Speech Emotion Recognition." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756323.

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Devi, S. Pandima, and K. S. Rekha. "Emotion Recognition from Recorded Speech." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921969.

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AS, Sai Puneeth Theja, Prasanna P, Prathush S, Mounika M, Vinod M, and Swetha M. "Speech Emotion Recognition Using Machine Learning." In Sri Venkatesa Perumal College of Engineering and Technology. International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/spav1663/svpcet24p17.

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The task of speech emotion recognition in human computer interaction is both fascinating and difficult. The practice of attempting to discern affective and emotional states in speech is known as speech emotion recognition. Speech emotion recognition is the process of accurately anticipating a person’s emotion from their speech. Many states, such as tone, pitch, expression, behaviour, etc., can be used to forecast an individual’s emotion. A select few of them are thought to be able to convey emotion through speaking. With the aid of feature extraction approaches that extract characteristics lik
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Sinha, Arryan, and G. Suseela. "Deep Learning-Based Speech Emotion Recognition." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-0892re.

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Speech Emotion Recognition, as described in this study, uses Neural Networks to classify the emotions expressed in each speech (SER). It’s centered upon concept where voice tone and pitch frequently reflect underlying emotion. Speech Emotion Recognition aids in the classification of elicited emotions. The MLP-Classifier is a tool for classifying emotions in a circumstance. As wave signal, allowing for flexible learning rate selection. RAVDESS (Ryerson Audio-Visual Dataset Emotional Speech and Song Database data) will be used. To extract the characteristics from particular audio input, Contrast
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Guder, Larissa, João Paulo Aires, and Dalvan Griebler. "Dimensional Speech Emotion Recognition: a Bimodal Approach." In Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/webmedia_estendido.2024.244402.

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Considering the human-machine relationship, affective computing aims to allow computers to recognize or express emotions. Speech Emotion Recognition is a task from affective computing that aims to recognize emotions in an audio utterance. The most common way to predict emotions from the speech is using pre-determined classes in the offline mode. In that way, emotion recognition is restricted to the number of classes. To avoid this restriction, dimensional emotion recognition uses dimensions such as valence, arousal, and dominance, which can represent emotions with higher granularity. Existing
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Guder, Larissa, João Paulo Aires, Felipe Meneguzzi, and Dalvan Griebler. "Dimensional Speech Emotion Recognition from Bimodal Features." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbcas.2024.2779.

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Considering the human-machine relationship, affective computing aims to allow computers to recognize or express emotions. Speech Emotion Recognition is a task from affective computing that aims to recognize emotions in an audio utterance. The most common way to predict emotions from the speech is using pre-determined classes in the offline mode. In that way, emotion recognition is restricted to the number of classes. To avoid this restriction, dimensional emotion recognition uses dimensions such as valence, arousal, and dominance to represent emotions with higher granularity. Existing approach
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Lorenzo Bautista, John, Yun Kyung Lee, Seungyoon Nam, Chanki Park, and Hyun Soon Shin. "Utilizing Dimensional Emotion Representations in Speech Emotion Recognition." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004283.

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Speech is a natural way of communication amongst humans and advancements in speech emotion recognition (SER) technology allow further improvement of human-computer interactions (HCI) with speech by understanding human emotions. SER systems are traditionally focused on categorizing emotions into discrete classes. However, discrete classes often overlook some subtleties between each emotion as they are prone to individual differences and cultures. In this study, we focused on the use of dimensional emotional values: valence, arousal, and dominance as outputs for an SER instead of the traditional
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Mittal, Raghav, Satya Vart, Prayag Shokeen, and Manoj Kumar. "Speech Emotion Recognition." In 2022 International Conference on Intelligent Technologies (CONIT). IEEE, 2022. http://dx.doi.org/10.1109/conit55038.2022.9848265.

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