Title :
Comparative Analysis of Prosodic Features and Linear Predictive Coefficients for Speaker Recognition Using Machine Learning Technique
Author :
Baidwan, V.S. ; Gujral, S.
Author_Institution :
Dept. of Comput. Sci. & Eng., Chandigarh Univ., Mohali, India
Abstract :
Speaker recognition is a biometric identification method that uses different features of individual´s voice for automatically identifying a speaker among a population. Two different features set for text dependent speaker recognition. A comparison is performed between Linear Predictive Coefficients (LPC) and Prosodic Features (F0, F1, F2, and F3) along with Radial Basis Function Network (RBFN) for recognizing a speaker population of 100 speakers. The results conclude that prosodic features performed better than LPC in terms of accuracy, precision and recall.
Keywords :
radial basis function networks; speaker recognition; F0 prosodic feature; F1 prosodic feature; F2 prosodic feature; F3 prosodic feature; LPC; RBFN; accuracy value; automatic speaker identification; biometric identification method; feature set; linear predictive coefficients; machine learning technique; precision value; radial basis function network; recall value; text dependent speaker recognition; voice features; Accuracy; Cepstrum; Feature extraction; Mel frequency cepstral coefficient; Speaker recognition; Speech; Speech recognition;
Conference_Titel :
Devices, Circuits and Communications (ICDCCom), 2014 International Conference on
Conference_Location :
Ranchi
DOI :
10.1109/ICDCCom.2014.7024705
