Model compensation for noises in training and test data

It is well known that the performance of speech recognition systems degrade rapidly as the mismatch between the training and test conditions increases. Approaches to compensate for this mismatch generally assume that the training data is noise-free, and the test data is noisy. In practice, this assumption is seldom correct. We propose an iterative technique to compensate for noise in both the training and test data. The adopted approach compensates the speech model parameters using the noise present in the test data, and compensates the test data frames using the noise present in the training data. The training and test data are assumed to come from different and unknown microphones and acoustic environments. The interest of such a compensation scheme has been assessed on the MASK task using a continuous density HMM-based speech recognizer. Experimental results show the advantage of compensating for both test and training noise