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

2026-06-17

AI Summary

This study addresses the significant performance degradation of foundation automatic speech recognition (ASR) models under narrowband channel conditions and in low-resource settings involving languages such as Hindi and accents like Indian English. The authors systematically evaluate the zero-shot capabilities of prominent open-source and commercial foundation ASR models on real-world narrowband telephone speech and investigate the effectiveness of fine-tuning with limited labeled data. Results demonstrate that zero-shot models generally perform poorly, while supervised fine-tuning with small amounts of data yields performance gains that vary substantially across languages and accents and are strongly dependent on the scale of pretraining data. The work identifies critical bottlenecks of foundation ASR systems in low-resource scenarios and quantifies the cross-lingual generalization potential of fine-tuning strategies.

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

Problem

• automatic speech recognition
• narrow-band
• low-resource language
• foundational models
• telephony conversations

Innovation

• narrow-band ASR
• low-resource languages
• foundational models
• fine-tuning
• zero-shot evaluation