Ten lectures on wavelets
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Real-time estimation of the parameters of long-range dependence
IEEE/ACM Transactions on Networking (TON)
Speech recognition: talk to the machine
IEEE Spectrum - They might be giants
Fractal dimension characterizes seizure onset in epileptic patients
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
Hi-index | 0.00 |
The performance of Hurst-Vectors (pH feature) for speaker identification systems is presented and discussed in this paper. The pH feature is a vector of Hurst (H) parameters obtained by applying a wavelet-based multi-dimensional estimator (M_dim_wavelets) to the windowed short-time segments of speech. The GMM (Gaussian Mixture Models) and the M_dim_fBm (multi-dimensional fractional Brownian motion) classification systems were considered in the performance analysis. The database—recorded from fixed and cellular phone channels— was uttered by 75 different speakers. The results have shown the superior performance of the M_dim_fBm classifier and that the pH feature aggregates new information on the speaker identity.