Summary
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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© 1999 Springer-verlag Berlin Heidelberg
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Lefèvre, F., Montacié, C., Caraty, MJ. (1999). K-Nearest Neighbours Estimator in a HMM-Based Recognition System. In: Ponting, K. (eds) Computational Models of Speech Pattern Processing. NATO ASI Series, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60087-6_10
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DOI: https://doi.org/10.1007/978-3-642-60087-6_10
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