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129
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EJASMP
2010
196views more  EJASMP 2010»
14 years 10 months ago
Optimizing Automatic Speech Recognition for Low-Proficient Non-Native Speakers
Computer Assisted Language Learning (CALL) applications for improving the oral skills of low-proficient learners have to cope with nonnative speech that is particularly challengin...
Joost van Doremalen, Catia Cucchiarini, Helmer Str...
CSL
2000
Springer
15 years 3 months ago
Efficient speech recognition using subvector quantization and discrete-mixture HMMS
This paper introduces a new form of observation distributions for hidden Markov models (HMMs), combining subvector quantization and mixtures of discrete distrib utions. Despite w...
Vassilios Digalakis, S. Tsakalidis, Costas Harizak...
136
Voted
ICASSP
2011
IEEE
14 years 7 months ago
Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition
In this paper, we present a novel technique for modeling the posterior probability estimates obtained from a neural network directly in the HMM framework using the Dirichlet Mixtu...
Balakrishnan Varadarajan, Garimella S. V. S. Sivar...
EACL
2009
ACL Anthology
16 years 4 months ago
Incremental Dialogue Processing in a Micro-Domain
This paper describes a fully incremental dialogue system that can engage in dialogues in a simple domain, number dictation. Because it uses incremental speech recognition and pros...
Gabriel Skantze, David Schlangen
124
Voted
NAACL
2007
15 years 5 months ago
A Geometric Interpretation of Non-Target-Normalized Maximum Cross-Channel Correlation for Vocal Activity Detection in Meetings
Vocal activity detection is an important technology for both automatic speech recognition and automatic speech understanding. In meetings, standard vocal activity detection algori...
Kornel Laskowski, Tanja Schultz