Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling

• URL: http://arxiv.org/abs/2412.16474v1
• Date: Sat, 21 Dec 2024 04:05:43 GMT
• Title: Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling
• Authors: Shao-Syuan Huang, Kuan-Po Huang, Andy T. Liu, Hung-yi Lee,
• Abstract summary: Whisper, one of the most advanced ASR models, handles 99 languages effectively. However, Whisper struggles with unseen languages, those not included in its pre-training. We propose methods that exploit these relationships to enhance ASR performance on unseen languages.
• Score: 50.62091603179394
• License:
• Abstract: Multilingual Automatic Speech Recognition (ASR) aims to recognize and transcribe speech from multiple languages within a single system. Whisper, one of the most advanced ASR models, excels in this domain by handling 99 languages effectively, leveraging a vast amount of data and incorporating language tags as prefixes to guide the recognition process. However, despite its success, Whisper struggles with unseen languages, those not included in its pre-training. Motivated by the observation that many languages share linguistic characteristics, we propose methods that exploit these relationships to enhance ASR performance on unseen languages. Specifically, we introduce a weighted sum method, which computes a weighted sum of the embeddings of language tags, using Whisper's predicted language probabilities. In addition, we develop a predictor-based approach that refines the weighted sum embedding to more closely approximate the true embedding for unseen languages. Experimental results demonstrate substantial improvements in ASR performance, both in zero-shot and fine-tuning settings. Our proposed methods outperform baseline approaches, providing an effective solution for addressing unseen languages in multilingual ASR.

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