Blind determination of the signal to noise ratio of speech signals based on estimation combination of multiple features

Russell Ondusko, Matthew Marbach, Andrew McClellan, Ravi P. Ramachandran, Linda M. Head, Mark C. Huggins, Brett Y. Smolenski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is 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 speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The aim is to correctly estimate SNR values from 0 to 30 dB, a range that is both practical and crucial for speaker identification systems. Additive white Gaussian noise and pink noise are investigated. The best feature for both white and pink noise is the vector of reflection coefficients which achieves an average SNR estimation error of 1.6 dB and 1.85 dB for white and pink noise respectively. Combining the estimates of 4 features lowers the error for white noise to 1.46 dB and for pink noise to 1.69 dB.

Original languageEnglish (US)
Title of host publicationAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems
Pages1895-1898
Number of pages4
DOIs
StatePublished - 2006
EventAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems - , Singapore
Duration: Dec 4 2006Dec 6 2006

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

Other

OtherAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems
CountrySingapore
Period12/4/0612/6/06

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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