TITLE:
A New Method of Voiced/Unvoiced Classification Based on Clustering
AUTHORS:
Mojtaba Radmard, Mahdi Hadavi, Mohammad Mahdi Nayebi
KEYWORDS:
Speech, Voiced, Unvoiced, Clustering, Cepstrum, Autocorrelation, Zero crossing
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.2 No.4,
November
16,
2011
ABSTRACT: In this paper, a new method for making v/uv decision is developed which uses a multi-feature v/uv classification algorithm based on the analysis of cepstral peak, zero crossing rate, and autocorrelation function (ACF) peak of short-time segments of the speech signal by using some clustering methods. This v/uv classifier achieved excellent results for identification of voiced and unvoiced segments of speech.