Literary and Colloquial Dialect Identification for Tamil using Acoustic Features

TL;DR

This paper presents an acoustic feature-based system using GMM and MFCC to identify Tamil dialects, aiding speech technology in dialect preservation without requiring annotated corpora.

Contribution

It introduces a dialect identification method that relies on acoustical features, eliminating the need for linguistic tools or annotated data, adaptable to other languages.

Findings

Achieved 12% error rate in dialect classification

Vowel nasalization contributes to classification accuracy

Performance varies with the number of GMM mixtures

Abstract

The evolution and diversity of a language is evident from it's various dialects. If the various dialects are not addressed in technological advancements like automatic speech recognition and speech synthesis, there is a chance that these dialects may disappear. Speech technology plays a role in preserving various dialects of a language from going extinct. In order to build a full fledged automatic speech recognition system that addresses various dialects, an Automatic Dialect Identification (ADI) system acting as the front end is required. This is similar to how language identification systems act as front ends to automatic speech recognition systems that handle multiple languages. The current work proposes a way to identify two popular and broadly classified Tamil dialects, namely literary and colloquial Tamil. Acoustical characteristics rather than phonetics and phonotactics are used, alleviating the requirement of language-dependant linguistic tools. Hence one major advantage of the proposed method is that it does not require an annotated corpus, hence it can be easily adapted to other languages. Gaussian Mixture Models (GMM) using Mel Frequency Cepstral Coefficient (MFCC) features are used to perform the classification task. The experiments yielded an error rate of 12%. Vowel nasalization, as being the reason for this good performance, is discussed. The number of mixture models for the GMM is varied and the performance is analysed.