Speaker Recognition from Coded Speech Using Support Vector Machines
Artur Janicki , Tomasz Staroszczyk
AbstractWe proposed to use support vector machines (SVMs) to recognize speakers from signal transcoded with different speech codecs. Experiments with SVM-based text-independent speaker classification using a linear GMM supervector kernel were presented for six different codecs and uncoded speech. Both matched (the same codec for creating speaker models and for testing) and mismatched conditions were investigated. SVMs proved to provide high accuracy of speaker recognition, however requiring higher number of Gaussian mixtures than in the baseline GMM-UBM system. In mismatched conditions the Speex codec was shown to perform best for creating robust speaker models.
|Publication size in sheets||0.5|
Habernal Ivan, Matoušek Václav (eds.): Text, Speech and Dialogue, Lecture Notes In Computer Science, no. 6836, 2011, Springer Berlin Heidelberg, ISBN 978-3-642-23537-5, 1-443 p., DOI:10.1007/978-3-642-23538-2
bfm%3A978-3-642-23538-2%2F1.pdf / 166.39 KB / No licence information
|Keywords in English||speaker recognition, speaker classification, speech coding, support vector machines. Artificial Intelligence (incl. Robotics), Database Management, Data Mining and Knowledge Discovery, Information Storage and Retrieval, Information Systems Applications (incl.Internet), speaker classification, speaker recognition, speech coding, support vector machines, User Interfaces and Human Computer Interaction|
|Publication indicators||= 13; = 11; = 27.0|
|Citation count*||25 (2020-03-25)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.