Union: a model for partial temporal corruption of speech

Abstract

This paper proposes a new statistical approach, namely the probabilistic union model, for speech recognition subjected to unknown burst noise during the utterance. The model combines the local temporal information based on the union of random events, to reduce the dependence of the model on information about the noise. This paper describes the theory of the model, and an implementation based on hidden Markov modeling techniques. For the evaluation, we used the TIDIGITS database for both isolated and connected digit recognition. The utterances were corrupted by various types of abrupt noise with unknown, time-varying characteristics. The experimental results indicate that the new model offers robustness to partial duration corruption, requiring no prior knowledge about the noise. A combination of the proposed model and conventional noise-reduction techniques is discussed, which has been shown to be potentially capable of dealing with a mixture of stationary noise and random, abrupt noise.