Abstract
A new method of text-dependent speaker identification using discriminative centroids weighting is proposed in this paper. The characteristics of the proposed method are as follows: feature parameters extraction, vector quantization with the growing cell structures (GCS) algorithm, stochastic fine-tuning of codebooks and discriminative centroids weighting (DCW) according to the uniqueness of personal features. The algorithm is evaluated on a database that includes 25 speakers each of them recorded in 24 different sessions. All 25 speakers spoke the same phrase for 240 times. The overall performance of the system was 99.5 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
B. Fritzke (1994): Growing Cell Structures — A self-organizing Network for Unsupervised and Supervised Learning. N.N., 7(9): 1441–1460.
B. Sabac, I. Gavat: Speaker Verification with Growing Cell Structures. accepted for publication at the EUROSPEECH’99, 5–9 September, Budapest, Hungary.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sabac, B., Gavat, I. (1999). Speaker Identification Using Discriminative Centroids Weighting — A Growing Cell Structure Approach. In: Matousek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds) Text, Speech and Dialogue. TSD 1999. Lecture Notes in Computer Science(), vol 1692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48239-3_31
Download citation
DOI: https://doi.org/10.1007/3-540-48239-3_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66494-9
Online ISBN: 978-3-540-48239-0
eBook Packages: Springer Book Archive