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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM20580

Speech Emotion Recognizer Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM20580

Volume:

7 |

5 |

May 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MM

Madhura Mohod

NM

Nisarga Mhaisane

AT

Aditi Thakare

SN

Sakshi Nichat

PVRT

Prof. Vaishali R. Thakare

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Abstract

In this project, a deep learning approach for emotion classification using speech data from different modalities is performed. A convolutional neural network(CNN) that captures discriminative information from audio features is trained to correctly classify the emotion labels. Further experiments also include experimenting with different network architectures of each individual model, using regularization strategies such as dropout, etc. To ensure that the trained model performs well on an unknown audio sample, different from the samples used for training and testing, audio samples from a completely separate dataset are collected and tested using the trained model. To ensure that the results obtained are indeed true, the Kaggle kernel1 used for training the models is also made public. Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it. The expression of emotions in human communication plays a very important role in the information that needs to be conveyed to the partner. The forms of expression of human emotions are very rich. It could be body language, facial expressions, eye contact, laughter, and tone of voice. The languages of the world’s peoples are different, but even without understanding a language in communication, people can almost understand part of the message that the other partner wants to convey with emotional expressions as mentioned. Among the forms of human emotional expression, the expression of emotions through voice is perhaps the most studied.

KEYWORDS

Speech Emotion Recognizer CNN Machine Learning Python Support Vector Machine(SVM).

Cite This Article

Madhura Mohod, Nisarga Mhaisane, Aditi Thakare, Sakshi Nichat, Prof. Vaishali R. Thakare, "Speech Emotion Recognizer Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023.

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Publication Date

8/4/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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