Robust Speech Analysis Based on Source-Filter Model Using Multivariate Empirical Mode Decomposition in Noisy Environments

This paper proposes a robust speech analysis method based on source-filter model using multivariate empirical mode decomposition (MEMD) under noisy conditions. The proposed method has two stages. At the first stage, magnitude spectrum of noisy speech signal is decomposed by MEMD into intrinsic mode functions (IMFs), and then IMFs corresponded to noise part are removed from them. At the second stage, log-magnitude spectrum of noise-reduced signals are decomposed into IMFs. Then, these are divided into two groups: the first group characterized by spectral fine structure for fundamental frequency estimation and the second group characterized by frequency response of vocal-tract filter for formant frequencies estimation. As opposed to the conventional linear prediction (LP) and cepstrum methods, the proposed method decomposes noise automatically in magnitude spectral domain and makes noise mixture become sparse in log-magnitude spectral domain. The results show that the proposed method outperforms LP and cepstrum methods under noisy conditions.