Accented Speech Recognition: A Survey

• URL: http://arxiv.org/abs/2104.10747v1
• Date: Wed, 21 Apr 2021 20:21:06 GMT
• Title: Accented Speech Recognition: A Survey
• Authors: Arthur Hinsvark (1), Natalie Delworth (1), Miguel Del Rio (1), Quinten McNamara (1), Joshua Dong (1), Ryan Westerman (1), Michelle Huang (1), Joseph Palakapilly (1), Jennifer Drexler (1), Ilya Pirkin (1), Nishchal Bhandari (1), Miguel Jette (1) ((1) Rev.com)
• Abstract summary: We present a survey of current promising approaches to accented speech recognition. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark makes research and comparison especially difficult.

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