Independent vector analysis followed by HMM-based feature enhancement for robust speech recognition

Abstract

This paper presents a feature-enhancement method that uses the outputs of independent vector analysis (IVA) for robust speech recognition. Although frequency-domain(FD) independent component analysis (ICA) can be successfully used in preprocessing of speech recognition because of its capability of blind source separation (BSS), the performance of the conventional ICA-based approaches is significantly degraded in underdetermined cases. Assuming the target speaker is located relatively close to microphones, the blind spatial subtraction array (BSSA) (Takahashi et al. [10]) tries to enhance target speech features by subtracting noise spectra estimated by FD ICA, even in the underdetermined cases. Unfortunately, the ICA may not be proficient at target speech estimation and then may cause inaccurate noise spectrum estimation. To improve robustness of speech recognition with the inaccurate noise spectra, we introduce Bayesian inference to estimate clean speech features. For a further improvement, FD ICA and delay-and-sum beamforming in the BSSA are replaced with IVA and its target speech output because IVA can improve separation performance without the permutation problem. Experimental results show that the proposed method can further reduce the relative word error rates by 60.11% and 20.07% on average compared to the BSSA for the AURORA2 and DARPA Resource Management databases, respectively.