  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.
