Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Two Stage Procedure for Phone Based Speaker Verfication
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Improving a GMM speaker verification system by phonetic weighting
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Searching through a speech memory for text-independent speaker verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Text-independent speaker verification: state of the art and challenges
Progress in nonlinear speech processing
| Hi-index | 0.00 |
In recent years, research in speaker verification has expended from using only the acoustic content of speech to trying to utilise high level features of information, such as linguistic content, pronunciation and idiolectal word usage. Phone based models have been shown to be promising for speaker verification, but they require transcribed speech data in the training phase. The present paper describes a segmental Gaussian Mixture Models (GMM) for text-independent speaker verification system based on data-driven Automatic Language Independent Speech Processing (ALISP). This system uses GMMs on a segmental level in order to exploit the different amount of discrimination provided by the ALISP classes. We compared the segmental ALISP-based GMM method with a baseline global GMM system. Results obtained for the NIST 2004 Speaker Recognition Evaluation data showed that the segmental approach outperforms the baseline system. It showed also that not all of the ALISP units are contributing to the discrimination between speakers.