A pattern recognition approach to signal to noise ratio estimation of speech

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise has been proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity. It is also highly useful as a pre-processing step in speech and speaker recognition systems so that a proper degree of enhancement can be applied to augment system performance. This paper is a work in progress depicting the investigation conducted by two undergraduate students pertaining to (1) further research in SNR estimation and (2) the preparation of a laboratory manual to be used in an undergraduate class.