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K-Nearest Neighbours Estimator in a HMM-Based System

Fabrice Lefevre 1 Claude Montacié 1 Marie-Josée Caraty 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : For many years, the K-Nearest Neighbours method (K-NN) has been known as one of the best probability density function (pdf) estimator [2]. The development of fast K-NN algorithms allows to reconsider its use in applications with large sample sets. In this outlook, the K-NN decision principle has been assessed on a frame by frame phonetic identification on the TIMIT database. Thereafter, a method to integrate the K-NN pdf estimator in a HMM-based system is proposed and tested on an acoustic-phonetic decoding task.
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Submitted on : Monday, August 14, 2017 - 4:57:54 PM
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Fabrice Lefevre, Claude Montacié, Marie-Josée Caraty. K-Nearest Neighbours Estimator in a HMM-Based System. NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, Jul 1997, St. Helier, Jersey, United Kingdom. pp.96-101, ⟨10.1007/978-3-642-60087-6_10⟩. ⟨hal-01574484⟩



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