关键词: 说话人识别, 希尔伯特黄变换（HHT）, 倒谱系数

Abstract: According to the problem that LPCC only reacts speech signal static characteristics and can not describe the low frequency local characteristics of speech signal well, a new speaker recognition algorithm based on HHT cepstrum coefficient is proposed. The low frequency local characteristics of the signal can be described better by the empirical mode decomposition of HHT. The dynamic characteristics are reacted by the Hilbert transform, improving the LPCC deficiencies. Speech signal is decomposed into intrinsic mode components using empirical mode decomposition. Hilbert transform is done for each component to get the Hilbert marginal spectrum. The logarithmic power spectrum of total marginal spectrum is calculated and then done the DCT to get 13-dimensional cepstrum coefficient. The feature is sent into the gaussian mixture model to do speaker recognition. Simulation results demonstrate that compared to the LPCC, the HHT cepstrum coefficient gets a higher recognition rate. Recognition rate is increased by 12.59%, but feature extraction time is increased by 19.27 s.

Key words: speaker recognition, Hilbert-Huang Transform（HHT）, cepstrum coefficient