title: A probabilistic union model with automatic order selection for noisy speech recognition.
abstract: A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.
authors.affiliation: School of Computer Science, The Queen's University of Belfast, United Kingdom. p.jancovic@qub.ac.uk
authors.forename: P
authors.initials: P
authors.lastname: Jancovic
authors.forename: J
authors.initials: J
authors.lastname: Ming
mesh_terms: D000465:Algorithms; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009622:Noise; D013067:Speech Perception