A Novel Approach to Identification of Speakers from Their Hum

Abstract

Automatic speaker recognition (ASR) deals with identification speakers with the help of machine from their voice. An ASR system will be efficient if the proper speaker-specific features are extracted. Most of the state-of-the-art ASR systems use the natural speech signal (either read speech or spontaneous or contextual speech) from the subjects. In this paper, an attempt is made to identify speakers from their hum. The experiments are shown for linear prediction coefficients (LPC), linear prediction cepstral coefficients (LPCC), and mel frequency cepstral coefficients (MFCC) as input feature vectors to the polynomial classifier of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> order approximation. Results are found to be better for MFCC than LP-based features.