On the use of a Family of Signal Limiters for Robust Recognition of Noisy Speech

The performance of a speech recognizer is often degraded by noise. Part of the reason for this performance degradation is due to the fact that there is often a strong mismatch between the training and testing conditions, i.e. the recognition features used to create models or reference templates in the training phase are vastly different from the features used in the testing condition because of the effects of the noise. One way to circumvent this mismatch problem is to use features that are less susceptible to changing noise conditions.