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» A Markov Random Field Model for Automatic Speech Recognition
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DAGM
2003
Springer
14 years 1 months ago
Improving Children's Speech Recognition by HMM Interpolation with an Adults' Speech Recognizer
In this paper we address the problem of building a good speech recognizer if there is only a small amount of training data available. The acoustic models can be improved by interpo...
Stefan Steidl, Georg Stemmer, Christian Hacker, El...
AIIA
2005
Springer
14 years 1 months ago
Multigranular Scale Speech Recognizers: Technological and Cognitive View
We present a proposal for an Automatic Speech Recognizer based on a “multigranular” model. The leading hypothesis is that speech signal contains information distributed on more...
Francesco Cutugno, Gianpaolo Coro, Massimo Petrill...
JCB
2006
215views more  JCB 2006»
13 years 7 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
INTERSPEECH
2010
13 years 2 months ago
Deep-structured hidden conditional random fields for phonetic recognition
We extend our earlier work on deep-structured conditional random field (DCRF) and develop deep-structured hidden conditional random field (DHCRF). We investigate the use of this n...
Dong Yu, Li Deng
CVPR
2004
IEEE
14 years 9 months ago
Asymmetrically Boosted HMM for Speech Reading
Speech reading, also known as lip reading, is aimed at extracting visual cues of lip and facial movements to aid in recognition of speech. The main hurdle for speech reading is th...
Pei Yin, Irfan A. Essa, James M. Rehg