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)...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE Signal Processing Society
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/36504 |
| Summary: | 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) 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-ed. 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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