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: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE Signal Processing Society
2011
|
| Online Access: | http://hdl.handle.net/20.500.11937/36504 |
| _version_ | 1848754788851777536 |
|---|---|
| author | Kuhne, M. Togneri, R. Nordholm, Sven |
| author_facet | Kuhne, M. Togneri, R. Nordholm, Sven |
| author_sort | Kuhne, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:45:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-36504 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:45:59Z |
| publishDate | 2011 |
| publisher | IEEE Signal Processing Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-365042017-09-13T16:08:47Z A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments Kuhne, M. Togneri, R. Nordholm, Sven 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. 2011 Journal Article http://hdl.handle.net/20.500.11937/36504 10.1109/TASL.2010.2048604 IEEE Signal Processing Society restricted |
| spellingShingle | Kuhne, M. Togneri, R. Nordholm, Sven A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title | A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title_full | A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title_fullStr | A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title_full_unstemmed | A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title_short | A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments |
| title_sort | new evidence model for missing data speech recognition with applications in reverberant multi-source environments |
| url | http://hdl.handle.net/20.500.11937/36504 |