Neural network classifiers and principal component analysis for blind signal to noise ratio estimation of speech signals

Matthew Marbach, Russell Ondusko, Ravi Ramachandran, Linda Head

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

3 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 neural network 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. Speech corrupted by additive white Gaussian noise, pink noise and two types of bandpass channel noise are investigated. The best individual feature is the vector of line spectral frequencies. Combination of the estimates of 3 features lowers the estimation error to an average of 3.69 dB for the four types of noise.

Original languageEnglish (US)
Title of host publication2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages97-100
Number of pages4
DOIs
StatePublished - Oct 26 2009
Event2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan, Province of China
Duration: May 24 2009May 27 2009

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
CountryTaiwan, Province of China
CityTaipei
Period5/24/095/27/09

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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