Improving Low Resource Code-switched ASR using Augmented Code-switched TTS

• URL: http://arxiv.org/abs/2010.05549v1
• Date: Mon, 12 Oct 2020 09:15:12 GMT
• Title: Improving Low Resource Code-switched ASR using Augmented Code-switched TTS
• Authors: Yash Sharma, Basil Abraham, Karan Taneja, Preethi Jyothi
• Abstract summary: Building Automatic Speech Recognition systems for code-switched speech has recently gained renewed attention. End-to-end systems require large amounts of labeled speech. We report significant improvements in ASR performance achieving absolute word error rate (WER) reductions of up to 5%.
• Score: 29.30430160611224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building Automatic Speech Recognition (ASR) systems for code-switched speech has recently gained renewed attention due to the widespread use of speech technologies in multilingual communities worldwide. End-to-end ASR systems are a natural modeling choice due to their ease of use and superior performance in monolingual settings. However, it is well known that end-to-end systems require large amounts of labeled speech. In this work, we investigate improving code-switched ASR in low resource settings via data augmentation using code-switched text-to-speech (TTS) synthesis. We propose two targeted techniques to effectively leverage TTS speech samples: 1) Mixup, an existing technique to create new training samples via linear interpolation of existing samples, applied to TTS and real speech samples, and 2) a new loss function, used in conjunction with TTS samples, to encourage code-switched predictions. We report significant improvements in ASR performance achieving absolute word error rate (WER) reductions of up to 5%, and measurable improvement in code switching using our proposed techniques on a Hindi-English code-switched ASR task.

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