Improvements of the beta-order minimum mean-square error (MMSE) spectral amplitude estimator using chi priors

Marek B. Trawicki, Michael T. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, the authors propose the Beta-Order Minimum Mean-Square Error (MMSE) Spectral Amplitude estimator with Chi statistical models for the speech priors. The new estimator incorporates both a shape parameter on the distribution and cost function parameter. The performance of the MMSE Beta-Order Spectral Amplitude estimator with Chi speech prior is evaluated using the Segmental Signal-to-Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ) objective quality measures. From the experimental results, the new estimator provides gains of 0-3 dB and 0-0.03 in SSNR and PESQ improvements over the corresponding MMSE Beta-Order MMSE Spectral Amplitude estimator with the standard Rayleigh statistical models for the speech prior.

Original languageEnglish
Title of host publication13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Pages938-941
Number of pages4
StatePublished - 2012
Event13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 - Portland, OR, United States
Duration: Sep 9 2012Sep 13 2012

Publication series

Name13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Volume2

Conference

Conference13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Country/TerritoryUnited States
CityPortland, OR
Period9/9/129/13/12

Keywords

  • Amplitude estimation
  • Parameter estimation
  • Phase estimation
  • Speech enhancement

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

Fingerprint

Dive into the research topics of 'Improvements of the beta-order minimum mean-square error (MMSE) spectral amplitude estimator using chi priors'. Together they form a unique fingerprint.

Cite this