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arXiv · 2606.18659

Responsible ASR: Overcoming Challenges of Foundational Models in Narrow-Band and Low-Resource Settings

Tejas Godambe·Nutan Choudhary·Sanket Shah·Nagaraj Adiga·Sharath Adavanne
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Abstract

Telephony conversations worldwide are conducted over narrow-band channels and are often spontaneous and colloquial in nature. This paper evaluates the performance of widely used foundational automatic speech recognition (ASR) models -- both open-source and commercial -- on narrow-band conversations in Hindi, a low-resource language, and Indian-accented English, a low-resource accent. We first assess these models in a zero-shot setting and find that their performance remains suboptimal across the board. Highlighting the challenges faced by ASR models in narrow-band and low-resource language scenarios, we further investigate the impact of fine-tuning open-source models using a limited set of real-life annotated recordings. Our findings indicate that while fine-tuning provides some improvements, its effectiveness varies across languages and accents, largely influenced by the amount of data encountered during pretraining

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Tejas Godambe, Nutan Choudhary, Sanket Shah, Nagaraj Adiga, Sharath Adavanne. 2026-06-17. Responsible ASR: Overcoming Challenges of Foundational Models in Narrow-Band and Low-Resource Settings. https://arxiv.org/abs/2606.18659

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