Speech Emotion Recognition using Machine Learning

Krishna, Kotikalapudi Vamsi; Sainath, Navuluri; Posonia, A. Mary

doi:10.1109/iccmc53470.2022.9753976

Cited by 23 publications

(3 citation statements)

References 12 publications

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“…Although it is also used with traditional ML techniques [137], it is commonly used for testing a type of DL models [135][136].…”

Section: Audiovisual Thai Emotion Database (Thai Db) [132]mentioning

confidence: 99%

A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition

Kshirod Sarmah

2024

jes

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In sophisticated Human-Computer Interfaces (HCI), the emotional state of the user is becoming a crucial component that is closely linked to emotional speech recognition. Spoken expressions, which can be a part of human-machine interaction, are an important source of emotional information. Speech emotion recognition (SER) in deep learning (DL) continues to be a hot topic, especially in the field of emotional computing. Current deep learning (DL) and neural network methods are applied in this highly active field of research. This is as a result of its expanding potential, advancements in algorithms, and practical uses. Quantitative factors such as pitch, intensity, accent and Mel-Frequency Cepstral Coefficients (MFCC) can be employed to model the paralinguistic data contained in human speech. To achieve SER, three key procedures are usually followed: data processing, feature selection/extraction, and classification based on the underlying emotional qualities. The nature of these processes and the peculiarities of human speech lend support to the employment of DL techniques for SER implementation. A variety of DL methods have been used for SER tasks in recent affective computing research works; however, only a small number of them capture the underlying ideas and methodologies that can be used to facilitate the three main steps of SER implementation. With a focus on the three SER implementation processes, we provide a state-of-the-art assessment of research conducted over the last ten years that tackled SER tasks from DL perspectives in this work. Various issues are covered in detail, including the problem of low classification accuracy of Speaker-Independent experiments and the related remedies. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines.

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“…Although it is also used with traditional ML techniques [137], it is commonly used for testing a type of DL models [135][136].…”

Section: Audiovisual Thai Emotion Database (Thai Db) [132]mentioning

confidence: 99%

A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition

Kshirod Sarmah

2024

jes

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“…Each node, except for the input nodes, has a non-linear activation function. An MLP uses backpropagation as a supervised learning technique and is composed of three types of layers as showen in figure 8: an input layer, a hidden layer, and an output layer [46].…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Real-Time Speech Emotion Recognition Using Deep Learning and Data Augmentation

Barhoumi¹,

Ayed

2023

Preprint

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In human-human interactions, detecting emotions is often easy as it can be perceived through facial expressions, body gestures, or speech. However, in human-machine interactions, detecting human emotion can be a challenge. To improve this interaction, the term 'speech emotion recognition' has emerged, with the goal of recognizing emotions solely through vocal intonation. In this work, we propose a speech emotion recognition system based on deep learning approaches and two efficient data augmentation techniques (noise addition and spectrogram shifting). To evaluate the proposed system, we used three different datasets: TESS, EmoDB, and RAVDESS. We employe several algorithms such as Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Mel spectrograms, Root Mean Square Value (RMS), and chroma to select the most appropriate vocal features that represent speech emotions. To develop our speech emotion recognition system, we use three different deep learning models, including MultiLayer Perceptron (MLP), Convolutional Neural Network (CNN), and a hybrid model that combines CNN with Bidirectional Long-Short Term Memory (Bi-LSTM). By exploring these different approaches, we were able to identify the most effective model for accurately identifying emotional states from speech signals in real-time situation. Overall, our work demonstrates the effectiveness of the proposed deep learning model, specifically based on CNN+BiLSTM, and the used two data augmentation techniques for the proposed real-time speech emotion recognition.

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“…If a classifier produces probabilistic outputs that indicate the probabilities a test instance belongs to an emotional class, then the probabilistic outputs from multiple classifiers can be combined in an efficient manner to accomplish improved accuracy in predicting the target classes. Support Vector Machines were used in several works for emotion recognition from speech, [6], [8], [9], [10], [19], [20], [21]. But the SVMs are not used with probabilistic outputs.…”

Section: Probabilistic Svm For Speech Emotion Recognitionmentioning

confidence: 99%

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

Nelapati

Selvarajan

2022

IJRITCC

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The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods.

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Speech Emotion Recognition using Machine Learning

Cited by 23 publications

References 12 publications

A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition

A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition

Real-Time Speech Emotion Recognition Using Deep Learning and Data Augmentation

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

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