Text-dependent speaker verification using subword neural tree networks

H.-S. Liou; Richard J. Mammone

doi:10.1117/12.191875

25 October 1994 Text-dependent speaker verification using subword neural tree networks

H.-S. Liou, Richard J. Mammone

Proceedings Volume 2277, Automatic Systems for the Identification and Inspection of Humans; (1994) https://doi.org/10.1117/12.191875
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

Citation Download Citation

H.-S. Liou and Richard J. Mammone "Text-dependent speaker verification using subword neural tree networks", Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); https://doi.org/10.1117/12.191875

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