A Robust Front-end for Speech Recognition Based on Computational Auditory Scene Analysis and Speaker Model
基于计算听觉场景分析和语者模型信息的语音识别鲁棒前端研究

Conventional noise robust speech recognition system does not work well when human speech is presented in the background. In this paper, a computational auditory scene analysis (CASA) and speaker model based speech segregation system is proposed to solve this problem. By utilizing speaker model and factorial-max vector quantization (MAXVQ) to estimate real-value masks in CASA framework, a robust front-end for speech recognition is constructed. Evaluations on speech separation challenge (SSC) showed that the proposed system won 15.68% improvement over the baseline system. The results of evaluation also proved the validity of the multi-speaker recognition and the real-value mask estimation module.