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Journal articles on the topic 'Technique and speech emotion recognition'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Liu, Zhen-Tao, Bao-Han Wu, Dan-Yun Li, Peng Xiao, and Jun-Wei Mao. "Speech Emotion Recognition Based on Selective Interpolation Synthetic Minority Over-Sampling Technique in Small Sample Environment." Sensors 20, no. 8 (2020): 2297. http://dx.doi.org/10.3390/s20082297.

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Speech emotion recognition often encounters the problems of data imbalance and redundant features in different application scenarios. Researchers usually design different recognition models for different sample conditions. In this study, a speech emotion recognition model for a small sample environment is proposed. A data imbalance processing method based on selective interpolation synthetic minority over-sampling technique (SISMOTE) is proposed to reduce the impact of sample imbalance on emotion recognition results. In addition, feature selection method based on variance analysis and gradient

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Nam, Youngja, and Chankyu Lee. "Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions." Sensors 21, no. 13 (2021): 4399. http://dx.doi.org/10.3390/s21134399.

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Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional speech denoising. In this study, a cascaded denoising CNN (DnCNN)–CNN architecture is proposed to classify emotions from Korean and German speech in noisy conditions. The proposed architecture consists of two stages. In the first stage, the DnCNN exploits the concept of residual learning to perform denoising; in the second stage, the CNN performs th

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Hazra, Sumon Kumar, Romana Rahman Ema, Syed Md Galib, Shalauddin Kabir, and Nasim Adnan. "Emotion recognition of human speech using deep learning method and MFCC features." Radioelectronic and Computer Systems, no. 4 (November 29, 2022): 161–72. http://dx.doi.org/10.32620/reks.2022.4.13.

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Subject matter: Speech emotion recognition (SER) is an ongoing interesting research topic. Its purpose is to establish interactions between humans and computers through speech and emotion. To recognize speech emotions, five deep learning models: Convolution Neural Network, Long-Short Term Memory, Artificial Neural Network, Multi-Layer Perceptron, Merged CNN, and LSTM Network (CNN-LSTM) are used in this paper. The Toronto Emotional Speech Set (TESS), Surrey Audio-Visual Expressed Emotion (SAVEE) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets were used for this

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Jung, Byung-Wook, Seung-Pyo Cheun, Youn-Tae Kim, and Sung-Shin Kim. "An Emotion Recognition Technique using Speech Signals." Journal of Korean Institute of Intelligent Systems 18, no. 4 (2008): 494–500. http://dx.doi.org/10.5391/jkiis.2008.18.4.494.

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Setyono, Jonathan Christian, and Amalia Zahra. "Data augmentation and enhancement for multimodal speech emotion recognition." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 3008–15. http://dx.doi.org/10.11591/eei.v12i5.5031.

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Humans’ fundamental need is interaction with each other such as using conversation or speech. Therefore, it is crucial to analyze speech using computer technology to determine emotions. The speech emotion recognition (SER) method detects emotions in speech by examining various aspects. SER is a supervised method to decide the emotion class in speech. This research proposed a multimodal SER model using one of the deep learning based enhancement techniques, which is the attention mechanism. Additionally, this research addresses the imbalanced dataset problem in the SER field using generative adv

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K, Deepak. "Improving Speech Recognition with Convolutional Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30472.

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This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accura

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Tiwari, Pradeep, and A. D. Darji. "A Novel S-LDA Features for Automatic Emotion Recognition from Speech using 1-D CNN." International Journal of Mathematical, Engineering and Management Sciences 7, no. 1 (2022): 49–67. http://dx.doi.org/10.33889/ijmems.2022.7.1.004.

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Emotions are explicit and serious mental activities, which find expression in speech, body gestures and facial features, etc. Speech is a fast, effective and the most convenient mode of human communication. Hence, speech has become the most researched modality in Automatic Emotion Recognition (AER). To extract the most discriminative and robust features from speech for Automatic Emotion Recognition (AER) recognition has yet remained a challenge. This paper, proposes a new algorithm named Shifted Linear Discriminant Analysis (S-LDA) to extract modified features from static low-level features li

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Kumar Nayak, Subrat, Ajit Kumar Nayak, Smitaprava Mishra, Prithviraj Mohanty, Nrusingha Tripathy, and Kumar Surjeet Chaudhury. "Exploring Speech Emotion Recognition in Tribal Language with Deep Learning Techniques." International journal of electrical and computer engineering systems 16, no. 1 (2025): 53–64. https://doi.org/10.32985/ijeces.16.1.6.

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Emotion is fundamental to interpersonal interactions since it assists mutual understanding. Developing human-computer interactions and a related digital product depends heavily on emotion recognition. Due to the need for human-computer interaction applications, deep learning models for the voice recognition of emotions are an essential area of research. Most speech emotion recognition algorithms are only deployed in European and a few Asian languages. However, for a low-resource tribal language like KUI, the dataset is not available. So, we created the dataset and applied some augmentation tec

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Baek, Ji-Young, and Seok-Pil Lee. "Enhanced Speech Emotion Recognition Using DCGAN-Based Data Augmentation." Electronics 12, no. 18 (2023): 3966. http://dx.doi.org/10.3390/electronics12183966.

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Although emotional speech recognition has received increasing emphasis in research and applications, it remains challenging due to the diversity and complexity of emotions and limited datasets. To address these limitations, we propose a novel approach utilizing DCGAN to augment data from the RAVDESS and EmoDB databases. Then, we assess the efficacy of emotion recognition using mel-spectrogram data by utilizing a model that combines CNN and BiLSTM. The preliminary experimental results reveal that the suggested technique contributes to enhancing the emotional speech identification performance. T

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K. P., Vaibhav. "Speech Based Emotion Recognition Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2093–95. http://dx.doi.org/10.22214/ijraset.2021.39420.

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Abstract: Speech emotion recognition is a trending research topic these days, with its main motive to improve the humanmachine interaction. At present, most of the work in this area utilizes extraction of discriminatory features for the purpose of classification of emotions into various categories. Most of the present work involves the utterance of words which is used for lexical analysis for emotion recognition. In our project, a technique is utilized for classifying emotions into Angry',' Calm', 'Fearful', 'Happy', and 'Sad' categories.

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Ismaiel, Wahiba, Abdalilah Alhalangy, Adil O. Y. Mohamed, and Abdalla Ibrahim Abdalla Musa. "Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13757–64. http://dx.doi.org/10.48084/etasr.7134.

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Today, automatic emotion recognition in speech is one of the most important areas of research in signal processing. Identifying emotional content in Arabic speech is regarded as a very challenging and intricate task due to several obstacles, such as the wide range of cultures and dialects, the influence of cultural factors on emotional expression, and the scarcity of available datasets. This study used a variety of artificial intelligence models, including Xgboost, Adaboost, KNN, DT, and SOM, and a deep-learning model named SERDNN. ANAD was employed as a training dataset, which contains three

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Anil Kumar, Chevella, Vumanthala Sagar Reddy, Ambati Pravallika, Rao Y. Chalapathi, and Neelam Syamala. "Analysis of human emotions through speech using deep learning fusion technique for Industry 5.0." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 316–27. http://dx.doi.org/10.11591/eei.v14i1.8464.

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Emotions are important for human well-being and social connections. This work focuses on the issue of effectively understanding emotions in human speech, specifically in the context of Industry 5.0. Traditional approaches and machine learning (ML) techniques for identifying emotions in speech are limited, such as the requirement for complicated feature extraction. Traditional methods yield recognition accuracies of no more than 90% because to the restricted extraction of temporal/sequence information. This paper suggests a ground-breaking fusion-based deep learning (DL) method to overcome thes

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Silviana, Widya Lestari, Kahar Saliyah, and Dwi Trismayanti. "Deep learning techniques for speech emotion recognition: A review." International Research Journal of Science, Technology, Education, and Management 3, no. 2 (2023): 78–91. https://doi.org/10.5281/zenodo.8139722.

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Speech emotion recognition is gaining significant importance in the domains of pattern recognition and natural language processing. In recent years, there has been notable progress in voice emotion detection within this field, primarily attributed to the successful application of deep learning techniques. Some research in this area lacks a thorough comparative study of different deep learning models and techniques related to speech emotion detection. This makes it difficult to identify the best performing approaches and their relative strengths and weaknesses. Therefore, the purpose of this wo

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Khedkar, Shilpa. "Activity Recommendation System Based on Emotion Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49641.

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Abstract—An Emotion Recognition-Based Activity Recommendation System aims at providing users with adequate activity recommendations based on emotional states using the latest developments within the scope of emotion recognition technology. This project would apply speech emotion recognition techniques, specifically focusing on the most current state of-the-art methods in Triangular Region Cut-Mix augmentation for the enhancement of accuracy of emotion classification while preserving audio spectrogram information related to key emotions. Furthermore, it involves a dual learning framework integr

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Chanchí Golondrino, Gabriel Elías, Liset Sulay Rodríguez Baca, and Luz Marina Sierra Martínez. "Speech Emotion Recognition Software System for Forensic Analysis." Ingeniería y Desarrollo 42, no. 01 (2024): 68–88. http://dx.doi.org/10.14482/inde.42.01.519.019.

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Affective computing aims to create systems capable of recognizing, processing, and si-mulating human emotions to enhance hu-man-computer interaction. Speech emo-tion recognition (SER) is a highly effective and non-invasive technique for assessing a user’s emotions by analyzing physiological variables. However, despite its widespread use in end-user perception identification, few applications have been developed in the field of forensic analysis. To address this gap, this research proposes a new fo-rensic emotion analysis software system, FOREMAN, based on the emotional study of the voice. The

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Triandi, Budi, Syahril Efendi, Herman Mawengkang, and Sawaluddin. "Regression-based Analytical Approach for Speech Emotion Prediction based on Multivariate Additive Regression Spline (MARS)." International Journal on Advanced Science, Engineering and Information Technology 13, no. 6 (2023): 2213–18. http://dx.doi.org/10.18517/ijaseit.13.6.18603.

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Using regression analysis techniques for speech-emotion recognition (SER) is an excellent method of resource efficiency. The labeled speech emotion data has high emotional complexity and ambiguity, making this research difficult. The maximum average difference is used to consider the marginal agreement between the source and target domains without focusing on the distribution of the previous classes in the two domains. To address this issue, we propose emotion recognition in speech using a regression analysis technique based on local domain adaptation. The results of this study show that the m

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Prathibha, Dr G., Y. Kavya, P. Vinay Jacob, and L. Poojita. "Speech Emotion Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36262.

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Speech is one of the primary forms of expression and is important for Emotion Recognition. Emotion Recognition is helpful to derive various useful insights about the thoughts of a person. Automatic speech emotion recognition is an active field of study in Artificial intelligence and Machine learning, which aims to generate machines that communicate with people via speech. In this work, deep learning algorithms such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are explored to extract features and classify emotions such as calm, happy, fearful, disgust, angry, neutral

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Pagidirayi, Anil Kumar, and Anuradha Bhuma. "Speech Emotion Recognition Using Machine Learning Techniques." Revue d'Intelligence Artificielle 36, no. 2 (2022): 271–78. http://dx.doi.org/10.18280/ria.360211.

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Mel Frequency Cepstral Coefficient (MFCC) method is a feature extraction technique used for speech signals. In machine learning systems, the Random Subspace Method (RSM) known as attribute bagging or bagged featuring used to classify the complete feature sets. In this paper, an innovative method is proposed which is a combination of RSM and kNN algorithm known as Subspace-kNN (S-kNN) classifier. The classifier selects the specific features extracted from MFCC are angry, sad, fear, disgust, calm, happiness, surprise, and neutral speech emotions in Speech Emotion Recognition (SER) system. Furthe

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Mustaqeem and Soonil Kwon. "A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition." Sensors 20, no. 1 (2019): 183. http://dx.doi.org/10.3390/s20010183.

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Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker’s emotional state from an individual’s speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recogn

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Roopa R, Harshitha Lakshmi N V, Dhana Lakshmi S, and Dilip B. "Enhanced Speech Emotion Recognition Using Hybrid Machine Learning and Deep Learning Models." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 194–99. https://doi.org/10.47001/irjiet/2025.iccis-202531.

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Abstract - In recent times, recognizing human emotions accurately has become crucial for enhancing humancomputer interaction. Speech Emotion Recognition (SER) enables systems to interpret emotional states from speech signals, improving applications such as virtual assistants, mental health monitoring, and affective computing. However, accurately classifying emotions remains a challenge due to the complexity of speech variations. In this paper, we propose a hybrid approach that integrates traditional machine learning techniques with deep learning models to improve emotion classification.

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Shirbhate, Tanvi, Devashish Deshmukh, Chetan Rajurkar, Sayali Sagane, and Prof. (Dr) Anup W. Burange. "Speech Emotion Recognition Using Machine Learning." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 2 (2024): 101–9. https://doi.org/10.5281/zenodo.11049046.

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<em>Language is the most important medium of communication. Emotions play an important role in human life. Recognizing emotion in speech is both important and challenging because we are dealing with human-computer interaction. Speech Emotion Recognition (SER) has many applications, and a lot of research has focused on this interest in recent years. Speech Emotion Recognition (SER) has become an important collaboration at the intersection of music processing and machine learning. The goal of the system is to identify and classify emotions in speech, leading to human-computer applications, psych

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Farooq, Misbah, Fawad Hussain, Naveed Khan Baloch, Fawad Riasat Raja, Heejung Yu, and Yousaf Bin Zikria. "Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network." Sensors 20, no. 21 (2020): 6008. http://dx.doi.org/10.3390/s20216008.

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Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (D

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Tajalsir, Mohammed, Susana Mu˜noz Hern´andez, and Fatima Abdalbagi Mohammed. "ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model." Signal & Image Processing : An International Journal 13, no. 1 (2022): 45–53. http://dx.doi.org/10.5121/sipij.2022.13104.

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When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique

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Dharmendra Kumar Roy, Naga Venkata Gopi Kumbha, Harender Sankhla, G. Teja Alex Raj, and Bashetty Akhilesh. "Deep Learning-Based Feature Extraction for Speech Emotion Recognition." international journal of engineering technology and management sciences 8, no. 3 (2024): 166–74. http://dx.doi.org/10.46647/ijetms.2024.v08i03.020.

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Emotion recognition from speech signals is an important and challenging component of Human-Computer Interaction. In the field of speech emotion recognition (SER), many techniques have been utilized to extract emotions from speech signals, including many well-established speech analysis and classification techniques. This model can be built by using various methods such as RNN, SVM, deep learning, cepstral coefficients, and various other methods, out of which SVM normally gives us the highest accuracy. We propose a model that can identify emotions present in the speech, which can be identified

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Sai Srinivas, T. Aditya, and M. Bharathi. "EmoSonics: Emotion Detection via Voice and Speech Recognition." JOURNAL OF COMPUTER SCIENCE AND SYSTEM SOFTWARE 1, no. 2 (2024): 1–7. http://dx.doi.org/10.48001/jocsss.2024.121-7.

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Understanding emotions from speech is like deciphering a rich tapestry of human expression in the realm of human-computer interaction. It's akin to listening to someone's tone and inflection to discern whether they're happy, surprised, or experiencing a range of other feelings. Researchers use a variety of techniques, from analyzing speech patterns to utilizing advanced technologies like fMRI, to decode these emotional cues. Emotions aren't just simple labels; they're complex and nuanced, demanding sophisticated methods for accurate interpretation. Some methods break emotions down into simple

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Sri Murugharaj B R, Shakthy B, Sabari L, and Kamaraj K. "Speech Based Emotion Recognition System." international journal of engineering technology and management sciences 7, no. 1 (2023): 332–37. http://dx.doi.org/10.46647/ijetms.2023.v07i01.050.

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Emotion reputation from speech alerts is a crucial yet difficult part of human-computer interaction (HCI). Several well-known speech assessment and type processes were employed in the literature on speech emotion reputation (SER) to extract emotions from warnings. Deep learning algorithms have recently been proposed as an alternative to conventional ones for SER. We develop a SER system that is totally based on exclusive classifiers and functions extraction techniques. Features from the speech alerts are utilised to train exclusive classifiers. To identify the broadest feasible appropriate cha

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Hatem, Ahmed Samit, and Abbas M. Al-Bakry. "The Information Channels of Emotion Recognition: A Review." Webology 19, no. 1 (2022): 927–41. http://dx.doi.org/10.14704/web/v19i1/web19064.

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Humans are emotional beings. When we express about emotions, we frequently use several modalities, whether we want to so overtly (i.e., Speech, facial expressions,..) or implicitly (i.e., body language, text,..). Emotion recognition has lately piqued the interest of many researchers, and various techniques have been studied. A review on emotion recognition is given in this article. The survey seeks single and multiple source of data or information channels that may be utilized to identify emotions and includes a literature analysis on current studies published to each information channel, as w

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Thiangtham, Chaidiaw, and Jakkree Srinonchat. "Speech Emotion Feature Extraction Using FFT Spectrum Analysis." Applied Mechanics and Materials 781 (August 2015): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amm.781.551.

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Speech Emotion Recognition has widely researched and applied to some appllication such as for communication with robot, E-learning system and emergency call etc.Speech emotion feature extraction is an importance key to achieve the speech emotion recognition which can be classify for personal identity. Speech emotion features are extracted into several coefficients such as Linear Predictive Coefficients (LPCs), Linear Spectral Frequency (LSF), Zero-Crossing (ZC), Mel-Frequency Cepstrum Coefficients (MFCC) [1-6] etc. There are some of research works which have been done in the speech emotion rec

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Bang, Jaehun, Taeho Hur, Dohyeong Kim, et al. "Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments." Sensors 18, no. 11 (2018): 3744. http://dx.doi.org/10.3390/s18113744.

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Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we pr

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A.Hammed, Fatima, and Loay George. "Using Speech Signal for Emotion Recognition Using Hybrid Features with SVM Classifier." Wasit Journal of Computer and Mathematics Science 2, no. 1 (2023): 27–38. http://dx.doi.org/10.31185/wjcm.102.

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Emotion recognition is a hot topic that has received a lot of attention and study,owing to its significance in a variety of fields, including applications needing human-computer interaction (HCI). Extracting features related to the emotional state of speech remains one of the important research challenges.This study investigated the approach of the core idea behind feature extraction is the residual signal of the prediction procedure is the difference between the original and the prediction .hence the visibility of using sets of extracting features from speech single when the statistical of lo

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Shuma, S., T. Christy Bobby, and S. Malathi. "EMOTION ANALYSIS USING SIGNAL AND IMAGE PROCESSING APPROACH BY IMPLEMENTING DEEP NEURAL NETWORK." Biomedical Sciences Instrumentation 57, no. 2 (2021): 313–21. http://dx.doi.org/10.34107/yhpn9422.04313.

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Emotion recognition is important in human communication and to achieve a complete interaction between humans and machines. In medical applications, emotion recognition is used to assist the children with Autism Spectrum Disorder (ASD to improve their socio-emotional communication, helps doctors with diagnosis of diseases such as depression and dementia and also helps the caretakers of older patients to monitor their well-being. This paper discusses the application of feature level fusion of speech and facial expressions of different emotions such as neutral, happy, sad, angry, surprise, fearfu

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Irhebhude, Martins E., Adeola O. Kolawole, and Mujtaba K. Tahi. "AN IMPROVED DEEP LEARNING TECHNIQUE FOR SPEECH EMOTION RECOGNITION AMONG HAUSA SPEAKERS." Science Journal of University of Zakho 13, no. 2 (2025): 186–97. https://doi.org/10.25271/sjuoz.2025.13.2.1433.

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This research addressed the challenge of recognizing emotions from speech by developing a deep learning-based speech-emotion recognition (SER) system. A key focus of the study is the creation of a new Hausa emotional speech dataset, aimed at addressing the linguistic and cultural imbalance in existing SER datasets, which predominantly feature Western languages. This study captured four emotions: happy, sad, angry, and surprise among native Hausa speakers. The self-captured dataset was recorded in an environment that is devoid of noise to ensure high quality and uniformity in the audio data. A

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Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim, and Ho-Da Kim. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6 (2021): 1997. http://dx.doi.org/10.3390/s21061997.

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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised,

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Bargavi S. K., Manju, Pawan Bhambu, and Mohan Vishal Gupta. "Spoken emotion recognition through human-computer interaction using a novel deep learning technology." Multidisciplinary Science Journal 5 (August 10, 2023): 2023ss0108. http://dx.doi.org/10.31893/multiscience.2023ss0108.

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The paradigm of textual or display-based control in human-computer interaction (HCI) has changed in favor of more understandable control methods, such as gesture, voice, and imitation. Speech in particular contains a large quantity of information, revealing the speaker's inner state as well as his or her goal and intention. The speaker's request can be understood through language analysis, but additional speech features show the speaker's mood, purpose, and intention. As a consequence, in modern HCI systems, emotion identification from speech has become crucial. Additionally, it is challenging

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Choudhary, Aachal. "Metaheuristically Enabled System for Emotion Recognition using BiLSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42333.

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Emotion recognition from speech is vital for applications in human-computer interaction and mental health diagnostics. This paper presents an efficient approach to classify emotions using the Toronto Emotional Speech Set (TESS) dataset. Key audio features, including Mel-Frequency Cepstral Coefficients (MFCCs) and spectral characteristics, are extracted to represent the speech signals. To enhance computational efficiency and mitigate overfitting, metaheuristic optimization techniques, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are utilized for dimensionality reductio

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Pavithra, Avvari, Sukanya Ledalla, J. Sirisha Devi, Golla Dinesh, Monika Singh, and G. Vijendar Reddy. "Deep Learning-based Speech Emotion Recognition: An Investigation into a sustainably Emotion-Speech Relationship." E3S Web of Conferences 430 (2023): 01091. http://dx.doi.org/10.1051/e3sconf/202343001091.

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Speech Emotion Recognition (SER) poses a significant challenge with promising applications in psychology, speech therapy, and customer service. This research paper proposes the development of an SER system utilizing machine learning techniques, particularly deep learning and recurrent neural networks. The model will be trained on a carefully labeled dataset of diverse speech samples representing various emotions. By analyzing crucial audio features such as pitch, rhythm, and prosody, the system aims to achieve accurate emotion recognition for novel speech samples. The primary objective of this

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Lee, Minjeong, and Miran Lee. "Performance Improvement of Speech Emotion Recognition Using ResNet Model with Data Augmentation–Saturation." Applied Sciences 15, no. 4 (2025): 2088. https://doi.org/10.3390/app15042088.

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Over the past five years, the proliferation of virtual reality platforms and the advancement of metahuman technologies have underscored the importance of natural interaction and emotional expression. As a result, there has been significant research activity focused on developing emotion recognition techniques based on speech data. Despite significant progress in emotion recognition research for the Korean language, a shortage of speech databases applicable to such research has been regarded as the most critical problem in this field, leading to overfitting issues in several models developed by

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Barua, Manya. "Decoding Emotions: Machine Learning Approach to Speech Emotion Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36178.

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Speech Emotion Recognition (SER) stands at the forefront of human-computer interaction, offering profound implications for fields such as healthcare, education, and entertainment. This project report delves into the application of Machine Learning (ML) techniques for SER, aiming to discern the emotional content from speech signals. The report begins with an overview of the significance of SER in various domains, emphasizing the need for accurate and robust emotion detection systems. Following this,a detailed exploration of the methodologies employed in SER is presented, encompassing feature ex

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Bang, Jae Hun, and Sungyoung Lee. "Adaptive Speech Emotion Recognition Framework Using Prompted Labeling Technique." KIISE Transactions on Computing Practices 21, no. 2 (2015): 160–65. http://dx.doi.org/10.5626/ktcp.2015.21.2.160.

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Pagidirayi, Anil Kumar, and B. Anuradha. "Speech feature extraction and emotion recognition using deep learning techniques." i-manager's Journal on Digital Signal Processing 12, no. 2 (2024): 1. https://doi.org/10.26634/jdp.12.2.21179.

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Speech Emotion Recognition (SER) is crucial for human-computer interaction, enabling systems to better understand emotions. Traditional feature extraction methods like Gamma Tone Cepstral Coefficients (GTCC) are used in SER for their ability to capture auditory features aligned with human hearing, but these methods fail to capture emotional nuances effectively. Mel Frequency Cepstral Coefficients (MFCC) have gained prominence for better representing speech signals in emotion recognition. This work introduces an approach combining traditional and modern techniques, comparing GTCC-based extracti

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