Title :
Automatic enlargement of speech corpus for speaker recognition
Author :
Alsulaiman, Mansour M.
Author_Institution :
Coll. of Comput. & Inf. Sci., King Saud Univ., Riyadh, Saudi Arabia
Abstract :
This research deals with the problem of recognition when only a few samples are available for training of the system. To avoid the low recognition rate caused by such type of speech corpus, automatic techniques for the enlargement of speech corpus are proposed in this paper. These techniques are: lengthening of sample by automatic segmentation, automatic noise addition at different sound-to-noise ratios (SNRs), and lengthening of reversed sample. Different combinations of samples, generated by the proposed techniques, are used to obtain the high recognition rate. These techniques have shown promising result.
Keywords :
Gaussian processes; cepstral analysis; hidden Markov models; speaker recognition; Gaussian mixture model; automatic noise addition; automatic segmentation; hidden Markov model; mel-frequency cepstral coefficients; sound-to-noise ratios; speaker recognition; speech corpus automatic enlargement; Databases; Hidden Markov models; Mel frequency cepstral coefficient; Noise; Speaker recognition; Speech; Training; Automatic segmentation; Database enlargement; HMM; MFCC; Samples generation; Speaker Recognition;
Conference_Titel :
Computers & Informatics (ISCI), 2011 IEEE Symposium on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-61284-689-7
DOI :
10.1109/ISCI.2011.5958931