Reliability-based estimation of the number of noisy features: application to model-order selection in the union models

This paper is concerned with the multi-stream approach in speech recognition. In a given set of feature streams, there may be some features corrupted by noise. Ideally, these features should be excluded from recognition. To achieve this, a-priori knowledge about the identity, including both the number and location, of the noisy features is required. We present a method for estimating the number of noisy feature streams. This method assumes no knowledge about the noise. It is based on calculation of the reliability of each feature stream and then evaluation of the joint maximal reliability. Since this method decreases the uncertainty about the noisy features and is statistical in nature, it can also be used to increase robustness of other classification systems. We present an application of this method to model-order selection in the union models. We performed tests on the TIDIGITS database, corrupted by noises affecting various numbers of feature streams. The experimental results show that this model achieves recognition performance similar to the one obtained with a-priori knowledge about the identity of the corrupted features.