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We present a joint noise and mask aware training strategy for deep neural network (DNN) based speech enhancement with sub-band features. First, based on the analysis of the previously proposed dynamic noise aware training approach tested on the wide-band (16 KHz) speech data, the full-band dynamic noise features cannot always improve the enhancement performance due to inaccurate noise estimation....
Based on the recently proposed speech pre-processing front-end with deep neural networks (DNNs), we first investigate different feature mapping directly from noisy speech via DNN for robust speech recognition. Next, we propose to jointly train a single DNN for both feature mapping and acoustic modeling. In the end, we show that the word error rate (WER) of the jointly trained system could be significantly...
In this paper, we present a synthesized stereo-based stochastic mapping approach for robust speech recognition. We extend the traditional stereo-based stochastic mapping (SSM) in two main aspects. First, the constraint of stereo-data, which is not practical in real applications, is relaxed by using HMM-based speech synthesis. Then we make feature mapping more focused on those incorrectly recognized...
In this paper, we present a novel approach to relax the constraint of stereo-data which is needed in a series of algorithms for noise-robust speech recognition. As a demonstration in SPLICE algorithm, we generate the pseudo-clean features to replace the ideal clean features from one of the stereo channels, by using HMM-based speech synthesis. Experimental results on aurora2 database show that the...
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