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Refinado por: assunto: Hidden Markov Models remover
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Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling
Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling
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Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling

Kingsbury, B.

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p.3761-3764

IEEE

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2
Boosted MMI for model and feature-space discriminative training
Boosted MMI for model and feature-space discriminative training
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Boosted MMI for model and feature-space discriminative training

Povey, D. ; Kanevsky, D. ; Kingsbury, B. ; Ramabhadran, B. ; Saon, G. ; Visweswariah, K.

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.4057-4060

IEEE

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3
Auto-encoder bottleneck features using deep belief networks
Auto-encoder bottleneck features using deep belief networks
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Auto-encoder bottleneck features using deep belief networks

Sainath, T. N. ; Kingsbury, B. ; Ramabhadran, B.

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, p.4153-4156

IEEE

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4
fMPE: discriminatively trained features for speech recognition
fMPE: discriminatively trained features for speech recognition
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fMPE: discriminatively trained features for speech recognition

Povey, D. ; Kingsbury, B. ; Mangu, L. ; Saon, G. ; Soltau, H. ; Zweig, G.

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2005, Vol.1, p.I/961-I/964 Vol. 1

IEEE

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5
Automatic speech recognition performance on a voicemail transcription task
Automatic speech recognition performance on a voicemail transcription task
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Automatic speech recognition performance on a voicemail transcription task

Padmanabhan, M. ; Saon, G. ; Huang, J. ; Kingsbury, B. ; Mangu, L.

IEEE transactions on speech and audio processing, 2002-10, Vol.10 (7), p.433-442

New York, NY: IEEE

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6
Constructing ensembles of ASR systems using randomized decision trees
Constructing ensembles of ASR systems using randomized decision trees
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Constructing ensembles of ASR systems using randomized decision trees

Siohan, O. ; Ramabhadran, B. ; Kingsbury, B.

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2005, Vol.1, p.I/197-I/200 Vol. 1

IEEE

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7
Making Deep Belief Networks effective for large vocabulary continuous speech recognition
Making Deep Belief Networks effective for large vocabulary continuous speech recognition
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Making Deep Belief Networks effective for large vocabulary continuous speech recognition

Sainath, T. N. ; Kingsbury, B. ; Ramabhadran, B. ; Fousek, P. ; Novak, P. ; Mohamed, A.-R

2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011, p.30-35

IEEE

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8
The IBM Attila speech recognition toolkit
The IBM Attila speech recognition toolkit
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The IBM Attila speech recognition toolkit

Soltau, H ; Saon, G ; Kingsbury, B

2010 IEEE Spoken Language Technology Workshop, 2010, p.97-102

IEEE

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9
An evaluation of a nonlinear feature transformation for conversational speech recognition
An evaluation of a nonlinear feature transformation for conversational speech recognition
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An evaluation of a nonlinear feature transformation for conversational speech recognition

Omar, M.K. ; Kingsbury, B.

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.1, p.I-785

IEEE

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10
Discriminative Training of Decoding Graphs for Large Vocabulary Continuous Speech Recognition
Discriminative Training of Decoding Graphs for Large Vocabulary Continuous Speech Recognition
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Discriminative Training of Decoding Graphs for Large Vocabulary Continuous Speech Recognition

Kuo, H. -K. J. ; Kingsbury, B. ; Zweig, G.

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007, Vol.4, p.IV-45-IV-48

IEEE

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