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EMNLP
2007
14 years 11 months ago
Learning Structured Models for Phone Recognition
We present a maximally streamlined approach to learning HMM-based acoustic models for automatic speech recognition. In our approach, an initial monophone HMM is iteratively refin...
Slav Petrov, Adam Pauls, Dan Klein
ICASSP
2009
IEEE
14 years 7 months ago
Unsupervised speaker adaptation for telephone call transcription
The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper inv...
R. Wallace, Kishan Thambiratnam, Frank Seide
INTERSPEECH
2010
14 years 4 months ago
Unsupervised discovery and training of maximally dissimilar cluster models
One of the difficult problems of acoustic modeling for Automatic Speech Recognition (ASR) is how to adequately model the wide variety of acoustic conditions which may be present i...
Françoise Beaufays, Vincent Vanhoucke, Bria...
ICASSP
2011
IEEE
14 years 1 months ago
Multi-view and multi-objective semi-supervised learning for large vocabulary continuous speech recognition
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
Xiaodong Cui, Jing Huang, Jen-Tzung Chien
ICASSP
2011
IEEE
14 years 1 months ago
Adapting acoustic and lexical models to dysarthric speech
Dysarthria is a motor speech disorder resulting from neurological damage to the part of the brain that controls the physical production of speech and is, in part, characterized by...
Kinfe Tadesse Mengistu, Frank Rudzicz