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22 July 2024
Reseach Article

Comparative Study of Techniques for Spoken Language Dialect Identification

by Ashwini G. Pawar, Nita V. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 7
Year of Publication: 2024
Authors: Ashwini G. Pawar, Nita V. Patil

Ashwini G. Pawar, Nita V. Patil . Comparative Study of Techniques for Spoken Language Dialect Identification. International Journal of Computer Applications. 186, 7 ( Feb 2024), 47-58. DOI=10.5120/ijca2024923414

@article{ 10.5120/ijca2024923414,
author = { Ashwini G. Pawar, Nita V. Patil },
title = { Comparative Study of Techniques for Spoken Language Dialect Identification },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 7 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 47-58 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923414 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-22T22:17:52.843728+05:30
%A Ashwini G. Pawar
%A Nita V. Patil
%T Comparative Study of Techniques for Spoken Language Dialect Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 7
%P 47-58
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

Identifying variations in spoken language resulting from regional or socioeconomic factors, known as dialect identification, is a significant problem in natural language processing and linguistics. This study contrasts various dialect identification approaches and evaluates their effectiveness. Techniques include deep learning, transfer learning, classical acoustic feature analysis, machine learning (ML) algorithms, phonetic and phonological analysis, lexical and grammatical feature extraction, prosodic analysis, and phonological analysis. Meticulous application of these techniques is applied to a hand-picked dataset of multiple dialects, with their performance assessed using accepted evaluation measures. These findings reveal that different techniques capture dialectal variations differently. Phonetic and phonological analysis excels at detecting minute pronunciation changes, while acoustic feature-based ML demonstrates resilience in dialect discrimination. Lexical and grammatical factors effectively recognize small differences in vocabulary and grammar usage. Prosodic features enhance dialect identification through intonation and rhythm patterns. Moreover, deep learning models showcase their capacity to learn intricate patterns from large datasets, and transfer learning techniques are effective in scenarios with limited dialect-specific data. Multilingual and cross-lingual approaches leverage shared linguistic properties for enhanced identification accuracy. Ensemble methods harness the strengths of multiple techniques, resulting in improved overall performance. This study underscores the significance of a diversified approach to dialect identification, with the choice of technique depending on factors such as available resources, data availability, and dialect complexity.

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Index Terms

Computer Science
Information Sciences
Natural Language Processing
Language Dialect Identification
Speech Processing.


Dialect identification deep learning acoustic analysis phonological analysis transfer learning multilingual approaches ensemble methods.