Study of Indian English Pronunciation Variabilities relative to Received Pronunciation

TL;DR

This paper analyzes Indian English pronunciation variabilities relative to Received Pronunciation using a large phonetic dataset, deriving data-driven phonetic rules and validating them through G2P conversion performance.

Contribution

It introduces a data-driven approach to characterize Indian English pronunciation variabilities and derives new phonetic rules from a large corpus, addressing limitations of qualitative analyses.

Findings

Derived new phonetic rules for Indian English pronunciation variabilities.

Validated rules improve Grapheme-to-Phoneme conversion accuracy.

Provided a comprehensive phonetic analysis of diverse Indian English speakers.

Abstract

Analysis of Indian English (IE) pronunciation variabilities are useful in building systems for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) synthesis in the Indian context. Typically, these pronunciation variabilities have been explored by comparing IE pronunciation with Received Pronunciation (RP). However, to explore these variabilities, it is required to have labelled pronunciation data at the phonetic level, which is scarce for IE. Moreover, versatility of IE stems from the influence of a large diversity of the speakers' mother tongues and demographic region differences. Prior linguistic works have characterised features of IE variabilities qualitatively by reporting phonetic rules that represent such variations relative to RP. The qualitative descriptions often lack quantitative descriptors and data-driven analysis of diverse IE pronunciation data to characterise IE on the phonetic level. To address these issues, in this work, we consider a corpus, Indic TIMIT, containing a large set of IE varieties from 80 speakers from various regions of India. We present an analysis to obtain the new set of phonetic rules representing IE pronunciation variabilities relative to RP in a data-driven manner. We do this using 15,974 phonetic transcriptions, of which 13,632 were obtained manually in addition to those part of the corpus. Furthermore, we validate the rules obtained from the analysis against the existing phonetic rules to identify the relevance of the obtained phonetic rules and test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.