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» Automatic speech recognition system channel modeling
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ICASSP
2009
IEEE
15 years 6 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
14 years 6 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»
15 years 4 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»
14 years 11 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