Nonlinear compensation for stochastic matching

The performance of an automatic speech recognizer degrades when there exists an acoustic mismatch between the training and the testing conditions in the data. Though it is certain that the mismatch is nonlinear, its exact form is unknown. Tackling the problem of nonlinear mismatches is a difficult task that has not been adequately addressed before. We develop an approach that uses nonlinear transformations in the stochastic matching framework to compensate for acoustic mismatches. The functional form of the nonlinear transformation is modeled by neural networks. We develop a new technique to train neural networks using the generalized EM algorithm. This technique eliminates the need for stereo databases, which are difficult to obtain in practical applications. The new technique is data-driven and hence can be used under a wide variety of conditions without a priori knowledge of the environment. Using this technique, we show that we can provide improvement under various types of acoustic mismatch; in some cases a 72% reduction in word error rate is achieved