Abstract :

Accent Recognition Plays A Critical Role In Enhancing The Performance Of Automatic Speech Recognition (ASR) Systems, Which Often Struggle With Accent Variations. This Paper Presents A Comprehensive Review Of Machine Learning (ML) And Deep Learning (DL) Techniques Applied To Accent Recognition. It Systematically Examines Preprocessing Methods, Feature Extraction Techniques, And Classification Models Used In The Literature. The Study Highlights The Dominance Of Mel-Frequency Cepstral Coefficients (MFCC) As A Feature Extraction Method And Discusses The Effectiveness Of Models Such As Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs), And Deep Architectures Like Convolutional Neural Networks (CNNs) And Long ShortTerm Memory (LSTM) Networks. Furthermore, The Paper Identifies Research Gaps, Including The Lack Of Standardized Datasets For Low-resource Languages And The Need For Robust Models That Generalize Across Diverse Accents. Future Directions Such As Cross-lingual Accent Classification, Generative Models, And Explainable AI Are Also Discussed. Keywords: Accent Recognition, Automatic Speech Recognition, Deep Learning, Machine Learning, Preprocessing, Feature Extraction.

Published:

23-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

156-163

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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