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ICASSP
2011
IEEE
12 years 10 months ago
Lattice-based unsupervised acoustic model training
Unsupervised acoustic model training has been successfully used to improve the performance of automatic speech recognition systems when only a small amount of manually transcribed...
Thiago Fraga-Silva, Jean-Luc Gauvain, Lori Lamel
MLMI
2004
Springer
14 years 2 days ago
The 2004 ICSI-SRI-UW Meeting Recognition System
We describe the ICSI-SRI-UW team’s entry in the Spring 2004 NIST Meeting Recognition Evaluation. The system was derived from SRI’s 5xRT Conversational Telephone Speech (CTS) r...
Chuck Wooters, Nikki Mirghafori, Andreas Stolcke, ...
ICASSP
2008
IEEE
14 years 1 months ago
Discriminative feature selection for hidden Markov models using Segmental Boosting
We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying featur...
Pei Yin, Irfan A. Essa, Thad Starner, James M. Reh...
ICASSP
2009
IEEE
13 years 10 months ago
Neural network based language models for highly inflective languages
Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture impo...
Tomas Mikolov, Jirí Kopecký, Lukas B...
LREC
2010
173views Education» more  LREC 2010»
13 years 8 months ago
Transcriber Driving Strategies for Transcription Aid System
Speech recognition technology suffers from a lack of robustness which limits its usability for fully automated speech-to-text transcription, and manual correction is generally req...
Grégory Senay, Georges Linares, Benjamin Le...