Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Voice recognition based human-computer interface design
Computers and Industrial Engineering
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
Fast algorithms for discrete and continuous wavelet transforms
IEEE Transactions on Information Theory - Part 2
Fingerprint recognition using model-based density map
IEEE Transactions on Image Processing
Fast fingerprint verification using subregions of fingerprint images
IEEE Transactions on Circuits and Systems for Video Technology
A general regression neural network
IEEE Transactions on Neural Networks
Speaker identification based on the frame linear predictive coding spectrum technique
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Speaker identification system using empirical mode decomposition and an artificial neural network
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper presents a study of driver's voice feature selection and classification for speaker identification in a vehicle security system. The proposed system consisted of a combination of feature extraction using continuous wavelet technique and voice classification using artificial neural network. In the feature extraction, a time-averaged wavelet spectrum based on continuous wavelet transform is proposed. Meanwhile, the artificial neural network techniques were used for classification in the proposed system. In order to verify the effect of the proposed system for classification, a conventional back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system. The identification rate is about 92% for using BPNN and 97% for using GRNN approach.