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  • Noise robustness is a key issue in successful deployment of automatic speech recognition systems in demanding environments such as hospital operating rooms. Perhaps the most successful way to overcome the additive noise obstacle is to employ a model adaptation scheme built around a set of dedicated clean speech and noise-only statistical models. Existing recognizer designs generally rely on relatively simple noise models, as more detailed ones would increase computational demands significantly. Simple models are, however, unable to provide accurate characterization of highly nonstationary noise present in real-world noisy facilities and thereby provide only limited reduction in error rate of the recognizer. The present article describes a novel approach to nonstationary acoustical noise modeling via a set of hierarchically tied hidden Markov models in a classification tree structure. Proposed statistical structure allows detailed description of nonstationary ambient acoustical noise while maintaining
  • Noise robustness is a key issue in successful deployment of automatic speech recognition systems in demanding environments such as hospital operating rooms. Perhaps the most successful way to overcome the additive noise obstacle is to employ a model adaptation scheme built around a set of dedicated clean speech and noise-only statistical models. Existing recognizer designs generally rely on relatively simple noise models, as more detailed ones would increase computational demands significantly. Simple models are, however, unable to provide accurate characterization of highly nonstationary noise present in real-world noisy facilities and thereby provide only limited reduction in error rate of the recognizer. The present article describes a novel approach to nonstationary acoustical noise modeling via a set of hierarchically tied hidden Markov models in a classification tree structure. Proposed statistical structure allows detailed description of nonstationary ambient acoustical noise while maintaining (en)
Title
  • Hierarchical classification tree modeling of nonstationary noise for robust speech recognition
  • Hierarchical classification tree modeling of nonstationary noise for robust speech recognition (en)
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  • Hierarchical classification tree modeling of nonstationary noise for robust speech recognition
  • Hierarchical classification tree modeling of nonstationary noise for robust speech recognition (en)
skos:notation
  • RIV/00216305:26220/10:PU88268!RIV11-GA0-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD102/08/H027), R, S, Z(MSM0021630513)
http://linked.open...iv/cisloPeriodika
  • 3
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 261480
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/10:PU88268
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • speech recognition, hidden Markov models, classification tree (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • LT - Litevská republika
http://linked.open...ontrolniKodProRIV
  • [72E53EEF175E]
http://linked.open...i/riv/nazevZdroje
  • Information Technology and Control
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 39
http://linked.open...iv/tvurceVysledku
  • Sigmund, Milan
  • Zelinka, Petr
http://linked.open...n/vavai/riv/zamer
issn
  • 1392-124X
number of pages
http://localhost/t...ganizacniJednotka
  • 26220
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