Towards Selection of Text-to-speech Data to Augment ASR Training

Shuo Liu; Leda Sarı; Chunyang Wu; Gil Keren; Yuan Shangguan; Jay Mahadeokar; Ozlem Kalinli

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arXiv · 2306.00998

Towards Selection of Text-to-speech Data to Augment ASR Training

Shuo Liu·Leda Sarı·Chunyang Wu·Gil Keren·Yuan Shangguan·Jay Mahadeokar·Ozlem Kalinli

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Abstract

This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic speech recognition (ASR) model. We trained a neural network, which can be optimised using cross-entropy loss or Arcface loss, to measure the similarity of a synthetic data to real speech. We found that incorporating synthetic samples with considerable dissimilarity to real speech, owing in part to lexical differences, into ASR training is crucial for boosting recognition performance. Experimental results on Librispeech test sets indicate that, in order to maintain the same speech recognition accuracy as when using all TTS data, our proposed solution can reduce the size of the TTS data down below its $30\,\%$, which is superior to several baseline methods.

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BibTeX RIS

Shuo Liu, Leda Sarı, Chunyang Wu, Gil Keren, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli. 2023-05-30. Towards Selection of Text-to-speech Data to Augment ASR Training. https://arxiv.org/abs/2306.00998

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