Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data

Alëna Aksënova; Zhehuai Chen; Chung-Cheng Chiu; Daan van Esch; Pavel Golik; Wei Han; Levi King; Bhuvana Ramabhadran; Andrew Rosenberg; Suzan Schwartz; Gary Wang

arXiv · 2205.08014

Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data

Alëna Aksënova·Zhehuai Chen·Chung-Cheng Chiu·Daan van Esch·Pavel Golik·Wei Han·Levi King·Bhuvana Ramabhadran·Andrew Rosenberg·Suzan Schwartz·Gary Wang

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Abstract

Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varieties, and also an understanding of model configuration that is the most helpful in achieving robust understanding of all types of speech. However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition. In this paper, we discuss recent progress towards developing more inclusive ASR systems, namely, the importance of building new data sets representing linguistic diversity, and exploring novel training approaches to improve performance for all users. We address recent directions within benchmarking ASR systems for accented speech, measure the effects of wav2vec 2.0 pre-training on accented speech recognition, and highlight corpora relevant for diverse ASR evaluations.

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Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu, Daan van Esch, Pavel Golik, Wei Han, Levi King, Bhuvana Ramabhadran, Andrew Rosenberg, Suzan Schwartz, Gary Wang. 2022-05-16. Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data. https://arxiv.org/abs/2205.08014

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