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

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.

Mots clés: nonparametric estimator; probability density function; HMM training; acoustic­ phonetic decoding; SNALC

Domaines: Informatique [cs]

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⟩