Gotowe bibliografie tematyczne / Technique and speech emotion recognition

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Gotowa bibliografia na temat „Technique and speech emotion recognition”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Technique and speech emotion recognition"

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Ramteke, Mr Ashwin. "Intelligent Speech Emotion Classification Using Deep Learning Technique." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5346–51. http://dx.doi.org/10.22214/ijraset.2023.52752.

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Abstract: Speech emotion recognition is a very interesting but very challenging human-computer interaction task. In recent years, this topic has attracted a lot of attention. In the field of speech emotion recognition, many techniques have been used to extract emotion from signals, including many well-established speech analysis and classification techniques. In the traditional way of speech emotion recognition, the emotion recognition features are extracted from the speech signals, and then the features, which are collectively known as the selection module, are selected, and then the emotions
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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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G, Apeksha. "Speech Emotion Recognition Using ANN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32584.

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The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset. According to it confusion matrix for model is created. The se
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Siddharth, Anand Avinash, and Upadhyay Pooja. "Deep Ganitrus Algorithm for speech emotion recognition." International Journal of Trends in Emerging Research and Development 2, no. 6 (2024): 116–21. https://doi.org/10.5281/zenodo.15068822.

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Modern automated speech recognition (ASR) challenges are much more difficult than their predecessors' due to the apparent need from practical applications. Over time, the ASR system has improved to handle a wider range of challenges, including a larger vocabulary, more freedom to express oneself, more background noise, more diverse speech, and more languages. There has been a lot of recent activity in the area of speech emotion recognition (SER), which seeks to identify emotional states from signals in spoken language The most recent paper suggests using a deep garnitures algorithm to identify
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Prasad, Dr Kanakam Siva Rama, N. Srinivasa Rao, and B. Sravani. "Advanced Model Implementation to Recognize Emotion Based Speech with Machine Learning." International Journal of Innovative Research in Engineering & Management 9, no. 6 (2022): 47–54. http://dx.doi.org/10.55524/ijirem.2022.9.6.8.

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Emotions are essential in developing interpersonal relationships. Emotions make emphasizing with others’ problems easy and leads to better communication without misunderstandings. Humans possess the natural ability of understanding others’ emotions from their speech, hand gestures, facial expressions etc and react accordingly but, it is impossible for machines to extract and understand emotions unless they are trained to do so. Speech Emotion Recognition is one step towards it, SER uses ML algorithms to forecast the emotion behind a speech. The features which include MEL, MFCC, and Chroma of a
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G, Apeksha. "Speech Emotion Recognition Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32388.

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The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset. According to it confusion matrix for model is created. The se
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C, Akalya Devi, Karthika Renuka D, Aarshana E. Winy, P. C. Kruthikkha, Ramya P, and Soundarya S. "2-D Attention Based Convolutional Recurrent Neural Network for Speech Emotion Recognition." International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) 3, no. 2 (2022): 163–72. http://dx.doi.org/10.34010/injiiscom.v3i2.8409.

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Recognizing speech emotions is a formidable challenge due to the complexity of emotions. The function of Speech Emotion Recognition(SER) is significantly impacted by the effects of emotional signals retrieved from speech. The majority of emotional traits, on the other hand, are sensitive to emotionally neutral elements like the speaker, speaking manner, and gender. In this work, we postulate that computing deltas for individual features maintain useful information which is mainly relevant to emotional traits while it minimizes the loss of emotionally irrelevant components, thus leading to fewe
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E.S, Pallavi. "Speech Emotion Recognition Based on Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33995.

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The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset.According to it confusion matrix for model is created. The fea
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Priyanka Joshi. "Hybrid Feature Extraction Technique with Data Augmentation for Speech Emotion Recognition Using Deep Learning." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 1005–16. https://doi.org/10.52783/jisem.v10i42s.8225.

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Prediction of emotions from human speech by machines is termed as speech emotion recognition. Speech is one of the most common and fastest methods of communication between humans. Speech emotion recognition (SER) by machines is a challenging task. Various deep learning algorithms are trying to make machines having such learning capabilities to achieve this task. Several researches are being conducted toward this area but identifying correct emotions from human speech is still challenging. The process of speech emotion recognition consists of three main stages –the feature extraction, feature s
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Atmaja, Bagus Tris, and Akira Sasou. "Effects of Data Augmentations on Speech Emotion Recognition." Sensors 22, no. 16 (2022): 5941. http://dx.doi.org/10.3390/s22165941.

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Data augmentation techniques have recently gained more adoption in speech processing, including speech emotion recognition. Although more data tend to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition in various conditions. The experiments are conducted on the Japanese Twitter-based emotional spee
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Rozprawy doktorskie na temat "Technique and 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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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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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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Dalili, Michael Nader. "Investigating emotion recognition and evaluating the emotion recognition training task, a novel technique to alter emotion perception in depression." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702458.

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Rationale. Accurately recognising facial expressions of emotion is important in social interactions and for maintaining interpersonal relationships. While comparing evidence across studies is difficult, research suggests that depressed individuals show deficits in emotion recognition (ER). A possible explanation for these deficits is the biased perception of these expressions. Research suggests that the emotion recognition training task, a novel cognitive bias modification (CBM) technique, shows promise in improving affect in individuals with low mood. However, further work is necessary to eva
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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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Książki na temat "Technique and 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, 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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Części książek na temat "Technique and speech emotion recognition"

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Gupta, Kakul, and Shailesh D. Kamble. "Speech emotion recognition optimization: A Bayesian approach." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003530190-71.

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Sasidharan Rajeswari, Sreeja, G. Gopakumar, and Manjusha Nair. "Speech Emotion Recognition Using Machine Learning Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6984-9_15.

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Chaurasiya, Akash, Govind Garg, Rahul Gaud, Bodhi Chakraborty, and Shashi Kant Gupta. "Recognition of Speech Emotion Using Machine Learning Techniques." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43145-6_12.

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Acharya, Mousumi, Shiba Charan Barik, and Sudhir Kumar Mohapatra. "Convolutional neural network approach to emotion recognition in speech." In Intelligent Computing Techniques and Applications. CRC Press, 2025. https://doi.org/10.1201/9781003658221-59.

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Nicolás, José Antonio, Javier de Lope, and Manuel Graña. "Data Augmentation Techniques for Speech Emotion Recognition and Deep Learning." In Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06527-9_27.

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Akinpelu, Samson, and Serestina Viriri. "Bi-Feature Selection Deep Learning-Based Techniques for Speech Emotion Recognition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77392-1_26.

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Gupta, Naman, and Nikunj Agarwal. "Application of Multilayer Perceptron in Speech Emotion Recognition Naman Gupta and Nikunj Agarwal." In Applied Soft Computing Techniques. Apple Academic Press, 2025. https://doi.org/10.1201/9781003593133-29.

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Waghmare, V. B., R. R. Deshmukh, and G. B. Janvale. "Emotions Recognition from Spoken Marathi Speech Using LPC and PCA Technique." In New Trends in Computational Vision and Bio-inspired Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_8.

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Kataria, Gaurav, Akansh Gupta, V. Sirish Kaushik, and Gopal Chaudhary. "Emotion Recognition from Speech Signals Using Machine Learning and Deep Learning Techniques." In EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76167-7_4.

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Biswas, Aditi, Sovon Chakraborty, Abu Nuraiya Mahfuza Yesmin Rifat, Nadia Farhin Chowdhury, and Jia Uddin. "Comparative Analysis of Dimension Reduction Techniques Over Classification Algorithms for Speech Emotion Recognition." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60036-5_12.

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Streszczenia konferencji na temat "Technique and speech emotion recognition"

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Upadhaya, Swetanshu, Rakesh Kumar, and Meenu Gupta. "Enhancing Speech Emotion Recognition Using Deep Learning Techniques." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731111.

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Hegde, Rajalaxmi, Sandeep Kumar Hegde, Ranjani, and Seema S. "Enhancing Emotion Recognition using Advanced Speech Processing Techniques." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894162.

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Padmavathi, Bvv, D. Ganesh, M. Sunil Kumar, Mungara Kiran Kumar, Vamsidhar Talasila, and B. Neelambaram. "Implementation of Speech Processing Techniques for Human Emotion Recognition." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717594.

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Khan, Sajid, Bushra Almas, Noshina Tariq, Farman Ul Haq, Amna Faisal, and Pranav Kumar. "Emotion Recognition of Human Speech Using Different Optimizer Techniques." In 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767445.

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Ruijie, Huang, and Wang Yuetian. "Leveraging Librosa for Speech Emotion Recognition: Techniques and Applications." In 2024 IEEE 4th International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2024. https://doi.org/10.1109/icdsca63855.2024.10859852.

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Sama, Bhavana, Bhukya Chandu Naik, and Geetha Guttikonda. "Performance of Speech Emotion Recognition Utilizing Machine Learning Techniques." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984493.

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Thakur, Sanjeev, Rohit Bele, Tarun Yarlagadda, Ved Prakash Chaubey, Shamneesh Sharma, and Saikat Gochhait. "Speech Emotion Recognition Using Deep Learning Techniques and Traditional Classifiers." In 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE, 2024. https://doi.org/10.1109/3ict64318.2024.10824482.

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Prathyakshini, Prathwini, and Keerthana. "Optimization of Speech Emotion Recognition Through Advanced Ensemble Learning Techniques." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859813.

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P, Siddique Ibrahim S., Gnaneswar Kurapati, Sai Tharun Chigurupati, Dhanush Kumar Reddy Panta, Kavya Sree V, and Nikhil VVNSS. "Human Speech Based Emotion and Gender Recognition Using Hybrid SVM and Deep Learning Technique." In 2024 First International Conference on Data, Computation and Communication (ICDCC). IEEE, 2024. https://doi.org/10.1109/icdcc62744.2024.10961435.

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Dere, Yash, Prasad Botre, Audumbar Telgar, and Pavan Kumar D. Paikrao. "Exploration and Optimization Technique for Speech Enhancement and Emotion Recognition Using Deep Neural Network." In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2025. https://doi.org/10.1109/iementech65115.2025.10959643.

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Artykuły w czasopismach Rozprawy doktorskie Książki
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