A speech enhancement system for automotive speech recognition with a hybrid voice activity detection method

This paper presents a front-end speech enhancement approach to robust speech recognition in automotive environments. It combines hybrid voice activity detection (VAD), relative transfer function (RT-F) based generalized sidelobe cancelation, and single-channel post filtering to enhance the speech signal of interest, thereby improving the robustness of speech recognition. First, we choose four typical driving scenarios, which include most of the noise types in automobiles to record training data. The recorded data is then used to train deep neural network models (DNNs) for both speech and noise. The trained DNNs are subsequently used to estimate the speech presence probability on a frame-by-frame basis. This speech presence probability is then combined with the output of an energy-based VAD to form a hybrid VAD, which serves as the basis for the rest components of the speech enhancement system, including RTF estimation, adaptive beamforming, and post-filtering. Experiments are conducted in real automotive environments. The results show that the developed method can significantly improve the performance of both VAD and automatic speech recognition (ASR).