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India is the second largest English-speaking country in the world with aspeaker base of roughly 130 million. Thus, it is imperative that automaticspeech recognition (ASR) systems for English should be evaluated on Indianaccents. Unfortunately, Indian speakers find a very poor representation inexisting English ASR benchmarks such as LibriSpeech, Switchboard, Speech AccentArchive, etc. In this work, we address this gap by creating Svarah, a benchmarkthat contains 9.6 hours of transcribed English audio from 117 speakers across65 geographic locations throughout India, resulting in a diverse range ofaccents. Svarah comprises both read speech and spontaneous conversational data,covering various domains, such as history, culture, tourism, etc., ensuring adiverse vocabulary. We evaluate 6 open source ASR models and 2 commercial ASRsystems on Svarah and show that there is clear scope for improvement on Indianaccents. Svarah as well as all our code will be publicly available.

Svarah: Evaluating English ASR Systems on Indian Accents