Relevant bibliographies by topics / Speech emotion recognition (SER)

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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 (SER)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Speech emotion recognition (SER)"

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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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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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Kumar, Balbant. "Speech Emotion Recognition using CNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45881.

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Abstract Speech Emotion Recognition (SER) is a growing area in affective computing that aims to detect and understand human emotions through speech signals. It finds extensive use in human-computer interaction, virtual assistants, mental health tracking, and automating customer service. This project introduced a deep learning method for SER utilizing Convolutional Neural Networks (CNNs). The system extracts mel-frequency cepstral coefficients (MFCCs) and spectrograms from raw audio inputs and transforms speech signals into two-dimensional images. These images were then processed by a CNN frame
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Kambale, Prof Jagdish, Abhijeet Khedkar, Prasad Patil, and Tejas Sonone. "Speech Emotion Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4829–33. http://dx.doi.org/10.22214/ijraset.2023.49703.

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Abstract: Due to different technical developments, speech signals have evolved into a kind of human-machine communication in the digital age. Recognizing the emotions of the person behind his or her speech is a crucial part of Human-Computer Interaction (HCI). Many methods, including numerous well-known speech analysis and classification algorithms, have been employed to extract emotions from signals in the literature on voice emotion recognition (SER). Speech Emotion Recognition (SER) approaches have become obsolete as the Deep Learning concept has come into play. In this paper, the algorithm
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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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Sondawale, Shweta. "Face and Speech Emotion Recognition System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5621–28. http://dx.doi.org/10.22214/ijraset.2024.61278.

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Abstract: Emotions serve as the cornerstone of human communication, facilitating the expression of one's inner thoughts and feelings to others. Speech Emotion Recognition (SER) represents a pivotal endeavour aimed at deciphering the emotional nuances embedded within a speaker's voice signal. Universal emotions such as neutrality, anger, happiness, and sadness form the basis of this recognition process, allowing for the identification of fundamental emotional states. To achieve this, spectral and prosodic features are leveraged, each offering unique insights into the emotional content of speech
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Gawali, Swayam. "Audio Aura - Speech Emotion Recognition System." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7082–88. https://doi.org/10.22214/ijraset.2025.70092.

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Abstract: Speech emotion recognition (SER) plays a crucial role in human-computer interaction, enabling systems to in- terpret and respond to user emotions effectively. In human- computer interaction, speech emotion recognition (SER) is es- sential because it allows systems to efficiently understand and react to user emotions. In this research, we introduce Audio Aura, a machine learning-based system for voice signal emotion classification. To improve classification accuracy and extractrich speech representations, the system uses a transformer-based model called Wav2Vec2. By leveraging Wav2Vec
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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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Kothuri, Jhansi. "Speech Emotion Recognition: An LSTM Approach." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45580.

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Abstract – This paper presents a novel approach to Speech Emotion Recognition (SER) utilizing a Long Short-Term Memory (LSTM) network to classify emotions from audio inputs in real-time. The primary goal of this research is to accurately identify various emotions, including happiness, sadness, anger, fear, and surprise, enhancing user experience in applications such as human-computer interaction, virtual assistants, and mental health monitoring. The methodology involves a comprehensive process that begins with the preprocessing of audio signals to ensure clarity and consistency. This is follow
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Dissertations / Theses on the topic "Speech emotion recognition (SER)"

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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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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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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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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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Books on the topic "Speech emotion recognition (SER)"

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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, 2016. http://dx.doi.org/10.1016/c2015-0-01959-1.

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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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Book chapters on the topic "Speech emotion recognition (SER)"

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SaiSree, Rampelly, Battula Pranavi, Chandhu Pullannagari, N. Srinivasa Reddy, and C. N. Sujatha. "Speech Emotion Recognition (SER) on Live Calls While Creating Events." In Advances in Computational Intelligence and Its Applications. CRC Press, 2024. http://dx.doi.org/10.1201/9781003488682-23.

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Patil, Ashvini, Krishnanjan Bhattacharjee, Archana Chougule, and Swati Mehta. "LSTM-Based Speech Emotion Recognition (SER) for Analyzing Patient’s Verbal Feedback." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7190-5_7.

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Palo, Hemanta Kumar, Debasis Behera, and Bikash Chandra Rout. "Comparison of Classifiers for Speech Emotion Recognition (SER) with Discriminative Spectral Features." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2774-6_10.

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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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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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Conference papers on the topic "Speech emotion recognition (SER)"

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Chou, Huang-Cheng. "A Tiny Whisper-SER: Unifying Automatic Speech Recognition and Multi-label Speech Emotion Recognition Tasks." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848651.

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Gomes, Vanessa M., Ana Patrícia F. M. Mascarenhas, Ivanoé J. Rodowanski, et al. "Using Speech and Text in Emotions Recognition." In 2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE). IEEE, 2024. https://doi.org/10.1109/sbr/wre63066.2024.10838143.

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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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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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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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Ferreira, Gabriel Gonçalves, and Johnny Marques. "A Speech Emotion Recognition Model to Detect Aggressive Behavior in Dialogues." In Anais Estendidos do Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2024. http://dx.doi.org/10.5753/sbsi_estendido.2024.238648.

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Speech Emotion Recognition (SER) is a multidisciplinary field that involves the development of computational models to automatically detect and analyze emotional states conveyed through speech signals. Utilizing techniques from signal processing, machine learning, and natural language processing, SER systems extract relevant features from audio data and classify emotions into distinct categories such as happiness, sadness, anger, and more. This work aims to leverage the latest SER techniques to build a robust model that can detect aggressive behavior in dialogues solely based on audio input si
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Li, Runnan, Zhiyong Wu, Jia Jia, Yaohua Bu, Sheng Zhao, and Helen Meng. "Towards Discriminative Representation Learning for Speech Emotion Recognition." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/703.

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In intelligent speech interaction, automatic speech emotion recognition (SER) plays an important role in understanding user intention. While sentimental speech has different speaker characteristics but similar acoustic attributes, one vital challenge in SER is how to learn robust and discriminative representations for emotion inferring. In this paper, inspired by human emotion perception, we propose a novel representation learning component (RLC) for SER system, which is constructed with Multi-head Self-attention and Global Context-aware Attention Long Short-Term Memory Recurrent Neutral Netwo
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Sayar, Alperen, Tuna Çakar, Tunahan Bozkan, Seyit Ertuğrul, and Fatma Gümüş. "Emotion Recognition from Speech via the Use of Different Audio Features, Machine Learning and Deep Learning Algorithms." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003279.

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Speech has been accepted as one of the basic, efficient and powerful communication methods. At the beginning of the 20th century, electroacoustic analysis was used for determining emotions in psychology. In academics, Speech Emotion Recognition (SER) has become one of the most studied and investigated research areas. This research program aims to determine the emotional state of the speaker based on speech signals. Significant studies have been undertaken during the last two decades to identify emotions from speech by using machine learning. However, it is still a challenging task because emot
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