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» Automatic speech recognition system channel modeling
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ICASSP
2009
IEEE
14 years 3 months ago
Combining mixture weight pruning and quantization for small-footprint speech recognition
Semi-continuous acoustic models, where the output distributions for all Hidden Markov Model states share a common codebook of Gaussian density functions, are a well-known and prov...
David Huggins-Daines, Alexander I. Rudnicky
TASLP
2011
13 years 4 months ago
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition
— Missing feature theory (MFT) has demonstrated great potential for improving the noise robustness in speech recognition. MFT was mostly applied in the log-spectral domain since ...
Maarten Van Segbroeck, Hugo Van Hamme
FPL
2001
Springer
90views Hardware» more  FPL 2001»
14 years 1 months ago
Implementing a Hidden Markov Model Speech Recognition System in Programmable Logic
Stephen J. Melnikoff, Steven F. Quigley, Martin J....
SPEECH
1998
171views more  SPEECH 1998»
13 years 8 months ago
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framewo...
Nagendra Kumar, Andreas G. Andreou