See the Silence: Improving Visual-Only Voice Activity Detection by Optical Flow and RGB Fusion

Abstract

In this work, we propose a novel approach for visual voice activity detection (VAD), which is an important component of audio-visual tasks such as speech enhancement. We focus on optimizing the visual component and propose a two-stream approach based on optical flow and RGB data. Both streams are analyzed by long short-term memory (LSTM) modules to extract dynamic features. We show that this setup clearly improves the one without optical flow. Additionally, we show that focusing on the lower face area is superior to processing the whole face, or only the mouth region as usually done. This aspect involves practical advantages, since it facilitates data labeling. Our approach especially improves the true negative rate, which means we detect frames without speech more reliably—we see the silence.