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ICASSP
2009
IEEE
14 years 3 months ago
Using collective information in semi-supervised learning for speech recognition
Training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. In this paper, we describe a novel semi-supervised learn...
Balakrishnan Varadarajan, Dong Yu, Li Deng, Alex A...
SEMCO
2007
IEEE
14 years 3 months ago
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
Discriminative training has been a leading factor for improving automatic speech recognition (ASR) performance over the last decade. The traditional discriminative training, howev...
Dong Yu, Li Deng
NAACL
1994
13 years 10 months ago
Speech Recognition Using a Stochastic Language Model Integrating Local and Global Constraints
In this paper, we propose a new stochastic language model that integrates local and global constraints effectively and describe a speechrecognition system basedon it. Theproposedl...
Ryosuke Isotani, Shoichi Matsunaga
INTERSPEECH
2010
13 years 3 months ago
Robust automatic speech recognition with decoder oriented ideal binary mask estimation
In this paper, we propose a joint optimal method for automatic speech recognition (ASR) and ideal binary mask (IBM) estimation in transformed into the cepstral domain through a ne...
Lae-Hoon Kim, Kyung-Tae Kim, Mark Hasegawa-Johnson
ICASSP
2010
IEEE
13 years 9 months ago
Improving speech recognition by explicit modeling of phone deletions
In a paper published by Greenberg in 1998, it was said that in conversational speech, phone deletion rate may go as high as 12% whereas syllable deletion rate is about 1%. The fi...
Tom Ko, Brian Mak