Using hidden Markov models based on autoregressive principles for isolated word recognition

Evgeny I. Bovbel; Polina P. Tkachova; Igor E. Kheidorov

doi:10.1117/12.357185

27 July 1999 Using hidden Markov models based on autoregressive principles for isolated word recognition

Evgeny I. Bovbel, Polina P. Tkachova, Igor E. Kheidorov

Proceedings Volume 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII; (1999) https://doi.org/10.1117/12.357185
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The purpose of this paper is to consider autoregressive hidden Markov models for the isolated words recognition task. The training and recognition algorithms for autoregressive hidden Markov models were developed and investigated. The speech feature vector was designed based on the perceptual psychoacoustical principles and arithmetic Fourier transform. The speech data base consisted from 200 belarussian words was created and used for experiments. The developed autoregressive hidden Markov model and introduced speech character vector provide a very high recognition performance.

Citation Download Citation

Evgeny I. Bovbel, Polina P. Tkachova, and Igor E. Kheidorov "Using hidden Markov models based on autoregressive principles for isolated word recognition", Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); https://doi.org/10.1117/12.357185

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