Streaming ASR with strict latency constraints is required in many speech
recognition applications. In order to achieve the required latency, streaming
ASR models sacrifice accuracy compared to non-streaming ASR models due to lack
of future input context. Previous research has shown that streaming and
non-streaming ASR for RNN Transducers can be unified by cascading causal and
non-causal encoders. This work improves upon this cascaded encoders framework
by leveraging two streaming non-causal encoders with variable input context
sizes that can produce outputs at different audio intervals (e.g. fast and
slow). We propose a novel parallel time-synchronous beam search algorithm for
transducers that decodes from fast-slow encoders, where the slow encoder
corrects the mistakes generated from the fast encoder. The proposed algorithm,
achieves up to 20% WER reduction with a slight increase in token emission
delays on the public Librispeech dataset and in-house datasets. We also explore
techniques to reduce the computation by distributing processing between the
fast and slow encoders. Lastly, we explore sharing the parameters in the fast
encoder to reduce the memory footprint. This enables low latency processing on
edge devices with low computation cost and a low memory footprint.

该研究提出了在流式自动语音识别中应用串联有因果和非因果编码器的方法，并使用两个有不同输入上下文大小和以不同的音频间隔产生输出的流式非因果编码器进行改进；同时提出了一种新的并行时间同步波束搜索算法。研究结果表明，在公共数据集 Librispeech 和内部数据集上，该算法在稍微增加音节延迟的情况下，实现 WER 降低 20％左右，并探索了分布式处理和共享参数降低内存占用的技术，以实现低计算成本和低内存占用的边缘设备上的低延迟处理。