FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization

Understand

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible.

• However, emitting fast without degrading quality, as measured by word error rate (WER), is highly challenging.
• Existing approaches including Early and Late Penalties and Constrained Alignments penalize emission delay by manipulating per-token or per-frame probability prediction in sequence transducer models.
• While being successful in reducing delay, these approaches suffer from significant accuracy regression and also require additional word alignment information from an existing model.