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» A hierarchical point process model for speech recognition
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ICASSP
2009
IEEE
14 years 2 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
ICASSP
2009
IEEE
14 years 2 months ago
Speech emotion recognition via a max-margin framework incorporating a loss function based on the Watson and Tellegen's emotion m
This paper considers a method for speech emotion recognition by a max-margin framework incorporating a loss function based on a well-known model called the Watson and Tellegen’s...
Sungrack Yun, Chang D. Yoo
COST
2009
Springer
203views Multimedia» more  COST 2009»
14 years 2 months ago
Multiple Feature Extraction and Hierarchical Classifiers for Emotions Recognition
Abstract. The recognition of the emotional states of speaker is a multidisciplinary research area that has received great interest in the last years. One of the most important goal...
Enrique M. Albornoz, Diego H. Milone, Hugo Leonard...
ICASSP
2009
IEEE
14 years 2 months ago
Unsupervised equalization of Lombard effect for speech recognition in noisy adverse environment
When exposed to environmental noise, speakers adjust their speech production to maintain intelligible communication. This phenomenon, called Lombard effect (LE), is known to consi...
Hynek Boril, John H. L. Hansen
ICASSP
2011
IEEE
12 years 11 months ago
Bayesian sensing hidden Markov models for speech recognition
We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
George Saon, Jen-Tzung Chien