NDLI: Similarity match (SM) technique for the development of client barcode

Content Provider	IEEE Xplore Digital Library
Author	Salleh, S.H.S.
Copyright Year	2000
Description	Author affiliation: Fak. Kejuruteraan Elektrik, Univ. Teknologi Malaysia, Johor, Malaysia (Salleh, S.H.S.)
Abstract	A hybrid neural network is proposed for speaker verification (SV). The basic idea in this system is the usage of vector quantization preprocessing as the feature extractor. The experiments were carried out using a neural network model (NNM) with frame labeling performed from a client codebook known as NNM-C. The work also examines how the neural network model with enhance features from the client barcode compares to NNM client codebook with linear time normalization (LTN). Improved performance for NNM (client barcode) with more inputs and proper alignment of the speech signals supports the hypothesis that a more detailed representation of the speech patterns proved helpful for the system. The flexibility of this system allows an equal error rate (EER) of 0.62% (speaker specific EER) on a single isolated digit and 1.9% (SI EER) on a sequence of 12 isolated digits.
Starting Page	144
Ending Page	148
File Size	413900
Page Count	5
File Format	PDF
ISBN	0780363558
DOI	10.1109/TENCON.2000.888407
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-24
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Samarium Neural networks Loudspeakers Frequency Vector quantization Natural languages Speech analysis Speech processing Speech recognition Electronic mail
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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