Accented Speech Recognition: A Survey

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.

References in corpus (5)

• Achieving Human Parity in Conversational Speech Recognition
• Progress & Compress: A scalable framework for continual learning
• Personalizing ASR for Dysarthric and Accented Speech with Limited Data
• A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition
• Leveraging native language information for improved accented speech recognition