A Phonetically Aware System for Speech Activity Detection

Abstract

Speech activity detection (SAD) is an essential component of most speech processing tasks and greatly influences the performance of the systems. Noise and channel distortions remain a challenge for SAD systems. In this paper, we focus on a dataset of highly degraded signals, developed under the DARPA Robust Automatic Transcription of Speech (RATS) program. On this challenging data, the best-performing systems are those based on deep neural networks (DNN) trained to predict speech/non-speech posteriors for each frame. We propose a novel two-stage approach to SAD that attempts to model phonetic information in the signal more explicitly than in current systems. In the first stage, a bottleneck DNN is trained to predict posteriors for senones. The activations at the bottleneck layer are then used as input to a second DNN, trained to predict the speech/non-speech posteriors. We test performance on two datasets, with matched and mismatched channels compared to those in the training data. On the matched channels, the proposed approach leads to gains of approximately 35% relative to our best single-stage DNN SAD system. On mismatched channels, the proposed system obtains comparable  performance to our baseline, indicating more work needs to be done to improve robustness to mismatched data.

Index Terms— Speech activity detection, deep neural networks, bottleneck features, degraded channels