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

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

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 probablity density function (pdf)...

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Bibliographic Details
Main Authors:	Kuhne, M., Togneri, R., Nordholm, Sven
Format:	Journal Article
Published:	IEEE Signal Processing Society 2011
Online Access:	http://hdl.handle.net/20.500.11937/36504

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