This paper compares a set of large scale support vector machine (SVM) training algorithms for language and speaker recognition tasks.We analyze five approaches for training phonetic and acoustic SVM models for language recognition. We compare the performance of these approaches as a function of the training time required by each of them to reach convergence, and we discuss their scalability towards large corpora. Two of these algorithms can be used in speaker recognition to train a SVM that classifies pairs of utterances as either belonging to the same speaker or to two different speakers. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations. State-of-the-art performance has been obtained in the female subset of the NIST 2010 Speaker Recognition Evaluation extended core test using a single SVM system.
Analysis of Large-Scale SVM Training Algorithms for Language and Speaker Recognition / Cumani, Sandro; Laface, Pietro. - In: IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING. - ISSN 1558-7916. - STAMPA. - 20:5(2012), pp. 1585-1596. [10.1109/TASL.2012.2186290]
Analysis of Large-Scale SVM Training Algorithms for Language and Speaker Recognition
CUMANI, SANDRO;LAFACE, Pietro
2012
Abstract
This paper compares a set of large scale support vector machine (SVM) training algorithms for language and speaker recognition tasks.We analyze five approaches for training phonetic and acoustic SVM models for language recognition. We compare the performance of these approaches as a function of the training time required by each of them to reach convergence, and we discuss their scalability towards large corpora. Two of these algorithms can be used in speaker recognition to train a SVM that classifies pairs of utterances as either belonging to the same speaker or to two different speakers. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations. State-of-the-art performance has been obtained in the female subset of the NIST 2010 Speaker Recognition Evaluation extended core test using a single SVM system.File | Dimensione | Formato | |
---|---|---|---|
Trans_2c_4_R1_5-open.pdf
accesso aperto
Tipologia:
1. Preprint / submitted version [pre- review]
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
554.05 kB
Formato
Adobe PDF
|
554.05 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2496064
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo