In conventional speaker recognition methods based on Mel-frequency cepstral coefficients (MFCCs), phase information has hitherto been ignored. In this paper, we propose a phase information extraction method that normalizes the change variation in the phase according to the frame position of the input speech and combines the phase information with MFCCs in text-independent speaker identification and verification methods. There is a problem with the original phase information extraction method when comparing two phase values. For example, the difference in the two values of π-\mathtildeθ1and \mathtildeθ2=-π+\mathtildeθ1is 2π-2\mathtildeθ1. If \mathtildeθ1≈ 0, then the difference ≈ 2π, despite the two phases being very similar to one another. To address this problem, we map the phase into coordinates on a unit circle. Speaker identification and verification experiments are performed using the NTT database which consists of sentences uttered by 35 (22 male and 13 female) Japanese speakers with normal, fast and slow speaking modes during five sessions. Although the phase information-based method performs worse than the MFCC-based method, it augments the MFCC and the combination is useful for speaker recognition. The proposed modified phase information is more robust than the original phase information for all speaking modes. By integrating the modified phase information with the MFCCs, the speaker identification rate was improved to 98.8% from 97.4% (MFCC), and equal error rate for speaker verification was reduced to 0.45% from 0.72% (MFCC), respectively. We also conducted the speaker identification and verification experiments on a large-scale Japanese Newspaper Article Sentences (JNAS) database, a similar trend as NTT database was obtained.

In conventional speaker recognition methods based on MFCC, phase information has been ignored. We proposed a method that integrated the phase information with MFCC on a speaker identification method, and a preliminary experiment was performed. In this paper, we propose a new modified feature parameter (that is, coordidates on an unit circle) obtained from the original phase information, and evaluated it by using speech database consisting of normal, fast and slow speaking modes. The speaker identification experiments were performed using NTT database which consists of sentences uttered by 35 Japanese speakers (22 males and 13 females) on five sessions over ten months. Each speaker uttered only 5 training utterances at a normal speaking mode (about 20 seconds in total). The proposed new phase information was more robust than the original phase information for all speaking modes. By integrating the new phase information with the MFCC, the speaker identification error rate was remarkably reduced for normal, fast and slow speaking rates in comparison with a standard MFCC-based method. In this paper, speaker verification experiments were also evaluated using the phase information. The experiments show that the phase information is also very useful for the speaker verification.