Feature extracted from wavelet decomposition using biorthogonal Riesz basis for text-independent speaker recognition

Authors:
Shung-Yung Lung
Affiliations:
Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan County, Taiwan
Venue:
Pattern Recognition
Year:
2008

Citing 7
Cited 4

Discrete-time signal processing

Discrete-time signal processing
Wavelets and subband coding

Wavelets and subband coding
Fuzzy Training Algorithm for Wavelet Codebook Based Text-Independent Speaker Identification

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Text-dependent Speaker Recognition using Wavelets and Neural Networks

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Rapid and brief communication: Wavelet feature domain adaptive noise reduction using learning algorithm for text-independent speaker recognition

Pattern Recognition
An improved lattice vector quantization scheme for waveletcompression

IEEE Transactions on Signal Processing
Data compression and harmonic analysis

IEEE Transactions on Information Theory

Rapid and brief communication: Wavelet feature domain adaptive noise reduction using learning algorithm for text-independent speaker recognition

Pattern Recognition
Efficient text independent speaker recognition with wavelet feature selection based multilayered neural network using supervised learning algorithm

Pattern Recognition
Improved wavelet feature extraction using kernel analysis for text independent speaker recognition

Digital Signal Processing
Average framing linear prediction coding with wavelet transform for text-independent speaker identification system

Computers and Electrical Engineering

Quantified Score

Hi-index	0.01

Visualization

Abstract

A new speaker feature extracted from wavelet decomposition using biorthogonal Riesz bases is described. Biorthogonal Riesz bases can offer a significant computational advantage by reducing the dimensionality of the eigenvalue problem at a not square matrix. Our results have shown that these wavelet Riesz bases introduced better performance than the other wavelet transforms with respect to the percentages of recognition.