Stereo-Based Stochastic Mapping for Robust Speech Recognition

We present a stochastic mapping technique for robust speech recognition that uses stereo data. The idea is based on building a GMM for the joint distribution of the clean and noisy channels during training and using an iterative compensation algorithm during testing. The proposed mapping was also interpreted as a mixture of linear transforms that are estimated in a special way using stereo data. The proposed method results in 28% relative improvement in string error rate (SER) for digit recognition in the car, and in about 10% relative improvement in word error rate (WER), when applied in conjunction with multi-style training (MST), for large vocabulary English speech recognition.