This paper presents a method of voiced/unvoiced (V/Uv) classification of noisy speech signals. Empirical mode decomposition (EMD), a newly developed tool to analyze nonlinear and non-stationary signals is used to filter the additive noise with the speech signal. The normalized autocorrelation of the filtered speech signal is computed to enhance the periodicity if any. It is considered that the voiced speech signal is periodically correlated and the unvoiced signal is not. A statistical model of determining periodic correlation is used to differentiate voiced and unvoiced speech with low SNR. The experimental results show that the use of EMD improves the classification performance and the overall efficiency is noticeable as compared to other existing algorithms.
Bibliographic reference. Molla, Md. Khademul Islam / Hirose, Keikichi / Minematsu, Nobuaki (2008): "Robust voiced/unvoiced speech classification using empirical mode decomposition and periodic correlation model", In INTERSPEECH-2008, 2530-2533.