  For speech interaction, voice activity detection (VAD) is often used as a
front-end. However, traditional VAD algorithms usually need to wait for a
continuous tail silence to reach a preset maximum duration before segmentation,
resulting in a large latency that affects user experience. In this paper, we
propose a novel semantic VAD for low-latency segmentation. Different from
existing methods, a frame-level punctuation prediction task is added to the
semantic VAD, and the artificial endpoint is included in the classification
category in addition to the often-used speech presence and absence. To enhance
the semantic information of the model, we also incorporate an automatic speech
recognition (ASR) related semantic loss. Evaluations on an internal dataset
show that the proposed method can reduce the average latency by 53.3% without
significant deterioration of character error rate in the back-end ASR compared
to the traditional VAD approach.
