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Text-independent speaker verification using Minimal Resource Allocation Networks.

Li Guojie¹, P Saratchandran, N Sundararajan

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

International Journal of Neural Systems

|February 17, 2005

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This study introduces a new speaker verification system, the Minimal Resource Allocation Network (MRAN), which uses less computational power. MRAN achieves comparable error rates to existing methods for accurate speaker identification.

Area of Science:

Speech processing
Machine learning
Biometrics

Background:

Speaker verification systems are crucial for security.
Existing Radial Basis Function (RBF) networks can be computationally intensive.

Purpose of the Study:

To present a novel text-independent speaker verification system using an online RBF network.
To evaluate the performance and efficiency of the proposed Minimal Resource Allocation Network (MRAN).

Main Methods:

Developed a Minimal Resource Allocation Network (MRAN), a sequential learning RBF.
Utilized LP-derived cepstral coefficients as feature vectors.
Compared MRAN with established RBF and Elliptical Basis Function (EBF) methods.

Main Results:

Related Experiment Videos

MRAN demonstrated comparable error rates to other speaker verification techniques.
MRAN exhibited significantly lower computational complexity.
Experiments were conducted on the TIMIT corpus with data from 258 speakers.

Conclusions:

MRAN offers an efficient alternative for text-independent speaker verification.
The system achieves high accuracy with reduced computational demands.
This approach is promising for practical biometric applications.