In the speaker recognition, when the cepstral coefficients are calculated from the LPC analysis parameters, the prediction error, or LPC residual signal, is usually ignored. However, there is an evidence that it contains a speaker specific information. The fundamental frequency of the speech signal or the pitch, which is usually extracted from the LPC residual, has been used for speaker recognition purposes, but because of the high intraspeaker variability of the pitch it is also often ignored. This paper describes our approach to integrating the pitch and LPC-residual with the LPC-cepstrum in a Gaussian Mixture Model (GMM) based speaker recognition system. The pitch and/or LPC-residual are considered as an additional features to the main LPC derived cepstral coefficients and are represented as a logarithm of the F0 and as a filter bank mel frequency cepstral (MFCC) vector respectively. The second task of this research was to verify whether the correlation between the different information sources is useful for the speaker recognition task. For the experiments we used the NTT database consisting of high quality speech samples. The speaker recognition system was evaluated in three modes-integrating only pitch or only LPC-residual and integrating both of them. The results showed that adding the pitch gives significant improvement only when the correlation between the pitch and cepstral coefficients is used. Adding only LPC-residual also gives significant improvement, but in contrast to the pitch, using the correlation with the cepstral coefficients does not have big effect. The best results we achieved using both the pitch and LPC-residual and are 98.5% speaker identification rate and 0.21% speaker verification equal error rate compared to 97.0% and 1.07% of the baseline system respectively.

http://web-ext.u-aizu.ac.jp/~markov/pubs/ASJ_99.pdf

Integrating pitch and LPC-residual information with LPC-cepstrum for text-independent speaker recognition

In a classical quantization system, each vector is represented by the nearest centroid; two vectors belonging to the same class are then indistinguishable. In order to mitigate this situation, we take into account the two nearest neighbours and define a “belonging degree” calculated from the distances between the vector and the two centroids. In the case of speaker independent speech recognition system, this “fuzzy” quantization gives better results.

Georges Zanellato

Papers

Speech recognition based on speech units

Speech recognition based on speech units.