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A Discrete Feature Vector for Endpoint Detection of Speech with Hidden Markov Model
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 Title & Authors
A Discrete Feature Vector for Endpoint Detection of Speech with Hidden Markov Model
Lee, Jei-Ky; Oh, Chang-Hyuck;
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The purpose of this paper is to suggest a discrete feature vector, robust in various levels of noisy environment and inexpensive in computation, for detection of speech segments and is to show such properties of the feature with real speech data. The suggested feature is one dimensional vector which represents slope of short term energies and is discretized into three values to reduce computational burden of computations in HMM. In experiments with speech data, the method with the suggested feature vector showed good performance even in noisy environments.
Endpoint detection;discretization;hidden Markov model;white noise;
 Cited by
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