Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition

• URL: http://arxiv.org/abs/2006.00782v1
• Date: Mon, 1 Jun 2020 08:16:24 GMT
• Title: Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
• Authors: Sanket Shah, Basil Abraham, Gurunath Reddy M, Sunayana Sitaram, Vikas Joshi
• Abstract summary: We show that fine-tuning ASR models on code-switched speech harms performance on monolingual speech. We propose regularization strategies for fine-tuning models for code-switching without sacrificing monolingual accuracy.
• Score: 14.559210845981605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, there has been significant progress made in Automatic Speech Recognition (ASR) of code-switched speech, leading to gains in accuracy on code-switched datasets in many language pairs. Code-switched speech co-occurs with monolingual speech in one or both languages being mixed. In this work, we show that fine-tuning ASR models on code-switched speech harms performance on monolingual speech. We point out the need to optimize models for code-switching while also ensuring that monolingual performance is not sacrificed. Monolingual models may be trained on thousands of hours of speech which may not be available for re-training a new model. We propose using the Learning Without Forgetting (LWF) framework for code-switched ASR when we only have access to a monolingual model and do not have the data it was trained on. We show that it is possible to train models using this framework that perform well on both code-switched and monolingual test sets. In cases where we have access to monolingual training data as well, we propose regularization strategies for fine-tuning models for code-switching without sacrificing monolingual accuracy. We report improvements in Word Error Rate (WER) in monolingual and code-switched test sets compared to baselines that use pooled data and simple fine-tuning.

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