Title
Speaker Recognition Employing Waveform Based Signal Representation In Nonorthogonal Multiple Transform Domains
Abstract
Automatic Speaker Recognition (ASR) technique employing Split Vector Quantized speech representation in multiple transform domains is presented. In this approach, a set of appropriate transform domains are selected and a vector quantized codebook is generated in each of these selected transform domains for the signal waveform. For each speaker, each signal vector is represented from the codebooks that yield the highest accuracy of representation. The algorithm is given and a performance measure is developed and used to evaluate the algorithm performance. Improved speech recognition accuracy was consistently obtained employing the proposed technique in comparison with vector quantization employing single transform VQ representations. Sample results for 10 speakers are presented to illustrate the considerable performance improvement for ASR.
Publication Date
1-1-2002
Publication Title
Proceedings - IEEE International Symposium on Circuits and Systems
Volume
2
Number of Pages
608-611
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ISCAS.2002.1011426
Copyright Status
Unknown
Socpus ID
0036292634 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0036292634
STARS Citation
Mikhael, Wasfy B. and Premakanthan, Pravinkumar, "Speaker Recognition Employing Waveform Based Signal Representation In Nonorthogonal Multiple Transform Domains" (2002). Scopus Export 2000s. 2964.
https://stars.library.ucf.edu/scopus2000/2964