NDLI: Non-linear input transformations for discriminative HMMs

Content Provider	IEEE Xplore Digital Library
Author	Johansen, F.T. Johnsen, M.H.
Copyright Year	1994
Description	Author affiliation: Dept of Telecommun., Inst. of Technol., Trondheim, Norway (Johansen, F.T.; Johnsen, M.H.)
Abstract	This paper deals with speaker-independent continuous speech recognition. Our approach is based on continuous density hidden Markov models with a non-linear input feature transformation performed by a multilayer perceptron. We discuss various optimisation criteria and provide results on a TIMIT phoneme recognition task, using single frame (mutual information or relative entropy) MMI embedded in Viterbi training, and a global MMI criterion. As expected, global MMI is found superior to the frame-based criterion for continuous recognition. We further observe that optimal sentence decoding is essential to achieve maximum recognition rate for models trained by global MMI. Finally, we find that the simple MLP input transformation, with five frames of context information, can increase the recognition rate significantly compared to just using delta parameters.<>
File Size	400807
File Format	PDF
ISBN	0780317750
ISSN	15206149
DOI	10.1109/ICASSP.1994.389314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech recognition Maximum likelihood decoding Speech processing Maximum likelihood estimation Artificial neural networks Multilayer perceptrons Viterbi algorithm Maximum likelihood detection Detectors
Content Type	Text
Resource Type	Article

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