NDLI: Joint maximum a posteriori estimation of transformation and hidden Markov model parameters

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Improved frequency marginal estimates for time-frequency distributions

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IF estimation of linear FM signals corrupted by multiplicative and additive noise: a time-frequency approach

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Nontrivial analytic signals with positive instantaneous frequency and band-limited amplitude

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Least squares deconvolution in wavelet domain for 1/f driven LTI systems

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Joint maximum a posteriori estimation of transformation and hidden Markov model parameters

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Joint maximum a posteriori estimation of transformation and hidden Markov model parameters

Content Provider	IEEE Xplore Digital Library
Author	Siohan, O. Chesta, C. Chin-Hui Lee
Copyright Year	2000
Description	Author affiliation: Lucent Technol. Bell Labs., Murray Hill, NJ, USA (Siohan, O.)
Abstract	Model adaptation techniques can usually be divided into indirect and direct approaches. On one hand, indirect or transformation-based techniques assume that a general transformation shared amongst different acoustic units is applied to clusters of model parameters. Such approaches (e.g. MLLR-maximum likelihood linear regression) are quite efficient when the amount of adaptation data is limited, but have poor asymptotic properties as the amount of adaptation data increases. On the other hand, direct adaptation approaches, like maximum a posteriori (MAP) estimation have nice asymptotic properties but provide only a moderate improvement when the amount of adaptation data is small. In this work, we jointly optimize a direct and indirect adaptation to take advantage of both approaches. Contrary to published approaches where direct and indirect adaptation are performed one after the other with a very loose interaction and no joint estimation criterion, we propose to estimate a MLLR-like transformation as well as the HMM mean vectors simultaneously, using a MAP estimation criterion. The optimal interaction between the direct and indirect adaptation associated with the prior knowledge provided by the MAP criterion leads to improvement over MLLR and MAP for all size of adaptation data evaluated.
File Size	452994
File Format	PDF
ISBN	0780362934
ISSN	15206149
DOI	10.1109/ICASSP.2000.859122
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-06-05
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum a posteriori estimation Hidden Markov models Adaptation model Maximum likelihood linear regression Automatic speech recognition Maximum likelihood estimation Multimedia communication Acoustic testing Automatic testing System testing
Content Type	Text
Resource Type	Article

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