Tandem Connectionist Feature Extraction for Conversational Speech Recognition

author: Qifeng Zhu, ICSI
published: Feb. 25, 2007,   recorded: June 2004,   views: 5025


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Multi-Layer Perceptrons (MLPs) can be used in automatic speech recognition in many ways. A particular application of this tool over the last few years has been the Tandem approach, as described by Hermansky et al in a number of publications. Here we discuss the characteristics of the MLP-based features used for the Tandem approach, and conclude with a report on their application to conversational speech recognition. The paper shows that MLP transformations yield variables that have regular distributions, which can be further modified by using logarithm to make the distribution easier to model by a Gaussian-HMM. Two or more vectors of these features can easily be combined without increasing the feature dimension. We also report recognition results that show that MLP features can significantly improve recognition performance for the NIST 2001 Hub-5 evaluation set with models trained on the Switchboard Corpus, even for complex systems incorporating MMIE training and other enhancements.

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Reviews and comments:

Comment1 Joey, June 19, 2007 at 1:24 p.m.:

The audio failed on this lecture after approx. 3mins 27secs. Is this a general problem with this lecture or is it just an issue for me?

Comment2 sahar, February 27, 2011 at 10:26 p.m.:

the same happened to me

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