Anil Vuppala

Priyanshi Pal, Shelly Jain, Anil Vuppala et al.

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.

Shelly Jain, Priyanshi Pal, Anil Vuppala et al.

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

Sai Akarsh, Vamshi Raghusimha, Anindita Mondal et al.

The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits accessibility, emphasizing the crucial need for translation into Indian languages. Despite existing Speech-to-Speech Machine Translation (SSMT) technologies, the lack of intonation in these systems gives monotonous translations, leading to a loss of audience interest and disengagement from the content. To address this, our paper introduces a dataset with stress annotations in Indian English and also a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech. This dataset is used for training a stress detection model, which is then used in the SSMT system for detecting stress in the source speech and transferring it into the target language speech. The TTS architecture is based on FastPitch and can modify the variances based on stressed words given. We present an Indian English-to-Hindi SSMT system that can transfer stress and aim to enhance the overall quality and engagement of educational content.