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Proceedings Paper

Text-dependent speaker verification using subword neural tree networks
Author(s): H.-S. Liou; Richard J. Mammone
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Paper Abstract

In this paper, a new algorithm for text-dependent speaker verification is presented. The algorithm uses a set of concatenated Neural Tree Networks (NTNs) trained with sub-word units for speaker verification. The conventional NTN when trained by all the words in training data achieves good results in the text-independent task. The proposed method is described as follows. First, the predetermined password in the training data is segmented into sub-word units by Hidden Markov Model (HMM). Second, an NTN is trained for only the data segmented into that sub-word unit. It integrates the discriminatory ability of NTN with the framework of HMMs which demonstrates ability in modeling temporal variation of speech. The sub-word NTN trained with clustered data reduces the complexity of the NTN structure, and is more powerful in discriminating speakers. This new algorithm was evaluated by experiments on a TI isolated-word database, which contains 16 speakers. An improvement of performance was obtained over baseline performance obtained from conventional methods.

Paper Details

Date Published: 25 October 1994
PDF: 8 pages
Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); doi: 10.1117/12.191875
Show Author Affiliations
H.-S. Liou, Rutgers Univ. (United States)
Richard J. Mammone, Rutgers Univ. (United States)

Published in SPIE Proceedings Vol. 2277:
Automatic Systems for the Identification and Inspection of Humans
Richard J. Mammone; J. David Murley Jr., Editor(s)

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