Towards Speaker Identification System based on Dynamic Neural Network
Keywords:Speech processing, neural networks, speaker recognition, multilayer perceptrons
AbstractThe conventional, Finite Impulse Response and Lattice-Ladder multilayer perceptron (MLP) structures with 4, 8 and 16 hidden neurons were verified for speaker identification. The experiments were performed on 10 speakers, 3 Lithuanian words, 7 sessions’ database. Identification performance was compared against two baseline methods: Vector Quantization (Linde-Buzo-Gray) and Gauss Mixture Models (Expectation Maximization). Increase of neuron number in hidden layer has led to smaller mean square errors on training dataset. A Finite Impulse Response MLP showed smaller mean square errors values. The results of experimental investigation show that neural networks can be used for speaker identification system as they outperform baseline methods. The best identification rate was archived by a multilayer perceptron with 4 hidden neurons and Finite Impulse Response MLP with 8 hidden neurons.
How to Cite
Ivanovas, E., & Navakauskas, D. (2012). Towards Speaker Identification System based on Dynamic Neural Network. Elektronika Ir Elektrotechnika, 18(10), 69-72. https://doi.org/10.5755/j01.eee.18.10.3066
SYSTEM ENGINEERING, COMPUTER TECHNOLOGY
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.
By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.