We introduce VoiceFilter-Lite, a single-channel source separation model that
runs on the device to preserve only the speech signals from a target user, as
part of a streaming speech recognition system. Delivering such a model presents
numerous challenges: It should improve the performance when the input signal
consists of overlapped speech, and must not hurt the speech recognition
performance under all other acoustic conditions. Besides, this model must be
tiny, fast, and perform inference in a streaming fashion, in order to have
minimal impact on CPU, memory, battery and latency. We propose novel techniques
to meet these multi-faceted requirements, including using a new asymmetric
loss, and adopting adaptive runtime suppression strength. We also show that
such a model can be quantized as a 8-bit integer model and run in realtime.

本论文介绍了一种能够实时运行于设备上的单通道源分离模型，其可以从一个目标说话者的语音信号中仅保留语音信息，以优化流式语音识别系统的表现。该模型具有分离源、单通道、语音识别、自适应运行时和实时这些特别之处，并通过使用一种新的不对称损失和采用自适应运行时抑制强度来实现这些目标。本论文最终证明了，这种模型可以被量化为 8 位整型模型，并且可以实时运行于设备上。