Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72675
Title: HMM speech recognition with reduced training
Authors: Foo, Say Wei 
Yap, Timothy
Issue Date: 1997
Source: Foo, Say Wei,Yap, Timothy (1997). HMM speech recognition with reduced training. Proceedings of the International Conference on Information, Communications and Signal Processing, ICICS 2 : 1016-1019. ScholarBank@NUS Repository.
Abstract: One of the problems faced in automatic speech recognition is the amount of training required to adapt the machine to the speaker way of pronunciation. To a certain extent, the accuracy of correct recognition is proportional to the amount of training and adaptation carried out. This is especially true when a large vocabulary is involved. For certain applications, it is desirable that the training requirement be reduced to the bare minimum without sacrificing the accuracy of recognition. In this paper, the minimum number of training required to achieve an acceptable degree of accuracy for a speaker dependent speech recognition system based on the Hidden Markov Model (HMM) is investigated. A method is also proposed which retains the same degree of accuracy of recognition with much reduced training.
Source Title: Proceedings of the International Conference on Information, Communications and Signal Processing, ICICS
URI: http://scholarbank.nus.edu.sg/handle/10635/72675
Appears in Collections:Staff Publications

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