Theses most similar to Neighborhood analysis methods in acoustic modeling for automatic speech recognition (Singh-Miller, Natasha, 1981-; 2010) read it

Articulatory features for robust visual speech recognition

Saenko, Ekaterina, 1976- (2004)

  • Advisors: Darrell, Trevor; James Glass
  • Department of Electrical Engineering and Computer Science
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Speech recognition system robustness to microphone variations

Chang, Jane W. (Jane Wen), 1970- (1995)

  • Advisor: Victor W. Zue
  • Department of Electrical Engineering and Computer Science
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Automatic acoustic measurement optimization for segmental speech recognition

Muzumdar, Manish D. (Manish Deepak) (1996)

  • Advisor: I. Lee Hetherington
  • Department of Electrical Engineering and Computer Science
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Speech recognition by clustering wavelet and PLP coefficients

Er, Chiangkai (1997)

  • Advisor: Kenneth Yip
  • Department of Electrical Engineering and Computer Science
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Large-margin Gaussian mixture modeling for automatic speech recognition

Chang, Hung-A (2008)

  • Advisor: Dr. James R. Glass
  • Department of Electrical Engineering and Computer Science
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Multimodal speech recognition with ultrasonic sensors

Zhu, Bo (2008)

  • Advisors: Dr. James R. Glass; Karen Livescu
  • Department of Electrical Engineering and Computer Science
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Parts-based models and local features for automatic speech recognition

Schutte, Kenneth Thomas, 1979- (2009)

  • Advisor: Dr. James R. Glass
  • Department of Electrical Engineering and Computer Science
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Bayesian distance metric learning on i-vector for speaker verification

Fang, Xia (2013)

  • Advisors: Dr. James R. Glass; Najim Dehak
  • Department of Electrical Engineering and Computer Science
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Discriminative training of acoustic models in a segment-based speech recognizer

Sandness, Eric D. (Eric David), 1979- (2000)

  • Advisor: I. Lee Hetherington
  • Department of Electrical Engineering and Computer Science
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Rapid speaker adaptation with speaker clustering

Pusateri, Ernest J. (Ernest James), 1978- (2002)

  • Advisor: Timothy J. Hazen
  • Department of Electrical Engineering and Computer Science
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