Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition
The major problem in distant-talking speech recognition is the corruption of speech signals by both interfering sounds and reverberation. While a range of successful techniques has been developed since the beginnings of speech recognition research to combat additive and short convolutive noise, comp...
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| Materiálatiipa: | Online |
| Giella: | eaŋgalasgiella |
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FAU University Press
2025
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| Liŋkkat: | ONIX_20250828T094736_9783944057620_38 |
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| _version_ | 1869517169072537600 |
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| author | Maas, Roland |
| author_browse | Maas, Roland |
| author_facet | Maas, Roland |
| author_sort | Maas, Roland |
| collection | Directory of Open Access Books |
| description | The major problem in distant-talking speech recognition is the corruption of speech signals by both interfering sounds and reverberation. While a range of successful techniques has been developed since the beginnings of speech recognition research to combat additive and short convolutive noise, compensating for long-term distortion caused by reverberation has not gained wide attention until recently. This thesis further develops an uncertainty decoding approach, named REverberation MOdeling for Speech recognition (REMOS), to adapt the acoustic model of a conventional Hidden Markov Model-based recognizer to reverberant environments. By incorporating a convolutive observation model, the Viterbi decoder is extended in order to implicitly provide a state-wise late reverberation estimate leading to a relaxation of the hidden Markov models' conditional independence assumption. The experimental evaluation confirms that REMOS yields strong speech recognition performance under noisy and reverberant conditions and furthermore allows for a rapid adaptation to changing acoustic conditions. |
| format | Online |
| id | doab-20.500.12854ir-166270 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1662702025-10-16T13:06:45Z Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition Maas, Roland Automatische Spracherkennung Hidden-Markov-Modell Nachhall thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes The major problem in distant-talking speech recognition is the corruption of speech signals by both interfering sounds and reverberation. While a range of successful techniques has been developed since the beginnings of speech recognition research to combat additive and short convolutive noise, compensating for long-term distortion caused by reverberation has not gained wide attention until recently. This thesis further develops an uncertainty decoding approach, named REverberation MOdeling for Speech recognition (REMOS), to adapt the acoustic model of a conventional Hidden Markov Model-based recognizer to reverberant environments. By incorporating a convolutive observation model, the Viterbi decoder is extended in order to implicitly provide a state-wise late reverberation estimate leading to a relaxation of the hidden Markov models' conditional independence assumption. The experimental evaluation confirms that REMOS yields strong speech recognition performance under noisy and reverberant conditions and furthermore allows for a rapid adaptation to changing acoustic conditions. 2025-08-29T05:08:23Z 2025-08-29T05:08:23Z 2025-08-28T08:00:05Z 2016 book ONIX_20250828T094736_9783944057620_38 https://library.oapen.org/handle/20.500.12657/105794 9783944057620 9783944057613 https://directory.doabooks.org/handle/20.500.12854/166270 eng FAU Forschungen : Reihe B open access image/jpeg image/jpeg n/a n/a https://library.oapen.org/bitstream/20.500.12657/105794/1/9783944057620.pdf https://library.oapen.org/bitstream/20.500.12657/105794/1/9783944057620.pdf FAU University Press 2c600dea-eece-4066-87be-da335e323fdb 9783944057620 9783944057613 AG Universitätsverlage 191 Erlangen open access |
| spellingShingle | Automatische Spracherkennung Hidden-Markov-Modell Nachhall thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes Maas, Roland Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title | Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title_full | Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title_fullStr | Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title_full_unstemmed | Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title_short | Uncertainty Decoding for Reverberation-Robust Automatic Speech Recognition |
| title_sort | uncertainty decoding for reverberation robust automatic speech recognition |
| topic | Automatische Spracherkennung Hidden-Markov-Modell Nachhall thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| topic_facet | Automatische Spracherkennung Hidden-Markov-Modell Nachhall thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| url | ONIX_20250828T094736_9783944057620_38 |
| work_keys_str_mv | AT maasroland uncertaintydecodingforreverberationrobustautomaticspeechrecognition |