HCRF-driven beamforming for reverberant speech recognition

Reverberation effects dramatically degrade the performance of automatic speech recognition (ASR). In conventional approaches, the microphone array-based beamformers try to filter rout the reverberation and re-build the enhanced speech signals for ASR based on the SSR (signal-to-reverberation) criterion. In such approach, the inconsistent criterions exists between the operation of array-based beamforming and model training of ASR. In this study, we propose a HCRF-driven approach for beamformer in which the acoustic models employ the framework of HCRFs and the adaptation of beamformer is based on the ASR output Both the models and the ASR result are incorporated into the adaptation process to iteratively estimate the parameters of an microphone array. A significant error rate reduction is obtained by the proposed method through the experimental results of RWCP room impulse database.