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arXiv · 2309.02459

Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation

Jiaxu Zhu·Weinan Tong·Yaoxun Xu·Changhe Song·Zhiyong Wu·Zhao You·Dan Su·Dong Yu·Helen Meng
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Abstract

Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) performance in new domains. However, the length of speech representation and text representation is inconsistent. Although the previous method up-samples the text representation to align with acoustic modality, it may not match the expected actual duration. In this paper, we proposed novel representations match strategy through down-sampling acoustic representation to align with text modality. By introducing a continuous integrate-and-fire (CIF) module generating acoustic representations consistent with token length, our ASR model can learn unified representations from both modalities better, allowing for domain adaptation using text-only data of the target domain. Experiment results of new domain data demonstrate the effectiveness of the proposed method.

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BibTeXRIS

Jiaxu Zhu, Weinan Tong, Yaoxun Xu, Changhe Song, Zhiyong Wu, Zhao You, Dan Su, Dong Yu, Helen Meng. 2023-10-07. Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation. https://doi.org/10.21437/interspeech.2023-1378

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