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

Streaming automatic speech recognition (ASR) aims to output each hypothesized word as quickly and accurately as possible. However, reducing latency while retaining accuracy is highly challenging. Existing approaches including Early and Late Penalties~\cite{li2020towards} and Constrained Alignment~\cite{sainath2020emitting} penalize emission delay by manipulating per-token or per-frame RNN-T output logits. While being successful in reducing latency, these approaches lead to significant accuracy degradation. In this work, we propose a sequence-level emission regularization technique, named FastEmit, that applies emission latency regularization directly on the transducer forward-backward probabilities. We demonstrate that FastEmit is more suitable to the sequence-level transducer~\cite{Graves12} training objective for streaming ASR networks. We apply FastEmit on various end-to-end (E2E) ASR networks including RNN-Transducer~\cite{Ryan19}, Transformer-Transducer~\cite{zhang2020transformer}, ConvNet-Transducer~\cite{han2020contextnet} and Conformer-Transducer~\cite{gulati2020conformer}, and achieve 150-300ms latency reduction over previous art without accuracy degradation on a Voice Search test set. FastEmit also improves streaming ASR accuracy from 4.4%/8.9% to 3.1%/7.5% WER, meanwhile reduces 90th percentile latency from 210 ms to only 30 ms on LibriSpeech.