K-NN Versus Gaussian in HMM-Based Recognition System

Abstract : For many years, the K-Nearest Neighbours method (K-NN) is known as one of the best probability density function (pdf) estimator. A fast K-NN algorithm has been developed and tested on the TIMIT database with a gain in computational time of 99;8%. The K-NN decision principle has been assessed on a frame by frame phonetic identification. A method to integrate K-NN estimator pdf in a HMM-based system is proposed and tested on an acoustic-phonetic decoding task. Finally, preliminary experiments are performed on the HMM topology inference.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01624700
Contributor : Lip6 Publications <>
Submitted on : Thursday, October 26, 2017 - 4:27:22 PM
Last modification on : Wednesday, May 15, 2019 - 10:12:14 AM

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  • HAL Id : hal-01624700, version 1

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Claude Montacié, Marie-Josée Caraty, Fabrice Lefevre. K-NN Versus Gaussian in HMM-Based Recognition System. Fifth European Conference on Speech Communication and Technology, EUROSPEECH 1997, Sep 1997, Rhodes, Greece. pp.529-532. ⟨hal-01624700⟩

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