Exploring Feature Enhancement in The Modulation Spectrum Domain via Ideal Ratio Mask for Robust Speech Recognition
Bicheng Yan, Meng-Che Wu, Berlin Chen · 2020
Development of robustness techniques is of paramount importance to the success of automatic speech recognition (ASR) systems. In this paper, we present a novel use of the ideal ratio mask (IRM) method to improve ASR robustness. IRM was originally proposed for time-frequency (T-F) masking-based speech enhancement and has shown considerable promise in preserving the intelligibility of a noisy mixture signal. Further, IRM is alternatively used to normalize the intermediate representations of speech feature vector sequences, in a holistic manner, for both training and test utterances. Finally, we instead treat IRM as a data augmentation method, conducted on speech feature vectors of training utterances or their intermediate representations, to generate additional augmented data for increasing the diversity of training data. A series of experiments carried out on the standard Aurora-4 database and task confirm the effectiveness of our methods.