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A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments

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3 Author(s)
Kühne, M. ; Sch. of Electr., Electron., & Comput. Eng., Univ. of Western Australia, Crawley, WA, Australia ; Togneri, R. ; Nordholm, S.

Conventional hidden Markov model (HMM) decoders often experience severe performance degradations in practice due to their inability to cope with uncertain data in time-varying environments. In order to address this issue, we propose the bounded-Gauss-Uniform mixture probability density function (pdf) as a new class of evidence model for missing data speech recognition. Exemplary for a hands-free speech recognition scenario, we illustrate how the parameters of the new mixture pdf can be estimated with the help of a multi-channel source separation front-end. In comparison with other models the new evidence pdf retains a fuller description of the available data and provides a more effective link between source separation and recognition. The superiority of the bounded-Gauss-Uniform mixture pdf over conventional approaches is demonstrated for a connected digits recognition task under varying test conditions.

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:19 ,  Issue: 2 )