Auxiliary Features from Laser-Doppler Vibrometer Sensor for Deep Neural Network Based Robust Speech Recognition

Recently, the signals captured from a laser Doppler vibrometer (LDV) sensor have shown the noise robustness to automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. In this study, an alternative approach, namely concatenating the auxiliary features extracted from the LDV signal with the conventional acoustic features, is proposed to further improve ASR performance based on the deep neural network (DNN) for acoustic modeling. The preliminary experiments on a small set of stereo-data including both LDV and acoustic signals demonstrate its effectiveness. Thus, to leverage more existing large-scale speech databases, a regression DNN is designed to map acoustic features to LDV features, which is well trained from a stereo-data set with a limited size and then used to generate pseudo-LDV features from a massive speech data set for parallel training of an ASR system. Our experiments verify that both the features from the limited scale LDV data set as well as the massive scale pseudo-LDV features can yield significant improvements of recognition performance over the system using purely acoustic features, in both quiet and noisy environments.