评估大模型在低资源窄带语音上的表现并提出改进方案
Responsible ASR: Overcoming Challenges of Foundational Models in Narrow-Band and Low-Resource Settings
- 在印度语和印式英语上测试主流ASR模型性能
- 微调可提升效果但受预训练数据量影响显著
- 揭示了窄带+低资源场景下的关键挑战
全球电话通话多通过窄带信道进行，且常为即兴口语。本文评估了广泛使用的开源与商用基础语音识别（ASR）模型在印地语（低资源语言）及印式英语（低资源口音）的窄带通话中的表现。首先在零样本设置下测试，发现所有模型性能均不理想。进一步研究使用有限真实录音微调开源模型的效果，结果表明微调虽带来一定提升，但成效因语言和口音而异，主要受预训练阶段数据量影响。
原文摘要 · Abstract (English)
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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