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» Improving Acoustic Models with Captioned Multimedia Speech
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TASLP
2010
122views more  TASLP 2010»
13 years 2 months ago
Error Approximation and Minimum Phone Error Acoustic Model Estimation
Minimum phone error (MPE) acoustic parameter estimation involves calculation of edit distances (errors) between correct and incorrect hypotheses. In the context of large vocabulary...
Matt Gibson 0002, Thomas Hain
CORR
2004
Springer
128views Education» more  CORR 2004»
13 years 7 months ago
Unsupervised Topic Adaptation for Lecture Speech Retrieval
We are developing a cross-media information retrieval system, in which users can view specific segments of lecture videos by submitting text queries. To produce a text index, the ...
Atsushi Fujii, Katunobu Itou, Tomoyosi Akiba, Tets...
CSL
2000
Springer
13 years 7 months ago
Pronunciation modeling by sharing Gaussian densities across phonetic models
Conversational speech exhibits considerable pronunciation variability, which has been shown to have a detrimental effect on the accuracy of automatic speech recognition. There hav...
Murat Saraclar, Harriet J. Nock, Sanjeev Khudanpur
LREC
2008
81views Education» more  LREC 2008»
13 years 9 months ago
Speech Errors on Frequently Observed Homophones in French: Perceptual Evaluation vs Automatic Classification
The present contribution aims at increasing our understanding of automatic speech recognition (ASR) errors involving frequent homophone or almost homophone words by confronting th...
Rena Nemoto, Ioana Vasilescu, Martine Adda-Decker
ICASSP
2011
IEEE
12 years 11 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