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A Model for Predicting Intelligibility of Binaurally Perceived Speech

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A Model for Predicting Intelligibility of Binaurally Perceived Speech
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    A Model for Predicting Intelligibility of Binaurally Perceived Speech by Angélique A. Scharine, Paula P. Henry, Mohan D. Rao, and Jason T. Dreyer ARL-TR-4075 April 2007 Approved for public release; distribution is unlimited.    NOTICES Disclaimers The findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an official endorsement or approval of the use thereof. DESTRUCTION NOTICE  ⎯  Destroy this report when it is no longer needed. Do not return it to the srcinator.    Army Research Laboratory Aberdeen Proving Ground, MD 21005-5425 ARL-TR-4075 April 2007 A Model for Predicting Intelligibility of Binaurally Perceived Speech Angélique A. Scharine and Paula P. Henry Human Research and Engineering Directorate, ARL Mohan D. Rao and Jason T. Dreyer Michigan Technological University Approved for public release; distribution is unlimited.  ii REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188   Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY)   April 2007 2. REPORT TYPE Final 3. DATES COVERED (From - To) October 2005 through September 2006 5a. CONTRACT NUMBER 5b. GRANT NUMBER 4. TITLE AND SUBTITLE A Model for Predicting Intelligibility of Binaurally Perceived Speech 5c. PROGRAM ELEMENT NUMBER 5d. PROJECT NUMBER 5e. TASK NUMBER 6. AUTHOR(S) Angélique A. Scharine and Paula P. Henry (both of ARL), Mohan D. Rao and Jason T. Dreyer (MTU) 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen Proving Ground, MD 21005-5425   8. PERFORMING ORGANIZATION REPORT NUMBER ARL-TR-4075   10. SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 11. SPONSOR/MONITOR'S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution is unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT Predicting and modeling intelligibility of monaurally or binaurally presented speech is difficult because it depends primarily on the accuracy and interdependency of frequency, time, and spatial information arriving at the listener. Despite these complex relationships, a new pragmatic model is suggested for speech mixed with broadband noise. A form of the logistic regression function is used to characterize human performance data. The regression of these signal properties onto empirical speech recognition performance data estimates the relationship of these properties to speech recognition. This concept is illustrated by the modeling of human performance on Central Institute for the Deaf W-22 speech items presented monaurally and binaurally in both reverberant and non-reverberant conditions at different signal-to-noise ratios. Although the implementation of the present model is limited to the data considered, it is expected that other data can be modeled after the procedure outlined in this report. The model described is the first step in developing an objective binaural measure for  predicting speech perception in noisy environments. 15. SUBJECT TERMS    binaural; objective measures; speech intelligibility 16. SECURITY CLASSIFICATION OF:   19a. NAME OF RESPONSIBLE PERSON   Angélique A. Scharine a. REPORT Unclassified b. ABSTRACT Unclassified c. THIS PAGE Unclassified 17. LIMITATIONOF ABSTRACT SAR 18. NUMBER OF PAGES 35 19b. TELEPHONE NUMBER (  Include area code ) 410-278-5957 Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18  iii Contents List of Figures v   List of Tables v   1.   Background 1  1.1 Existing Objective Measures of Speech Intelligibility....................................................1 1.2 How Binaural Measurement Can Improve Objective Tests............................................2 1.3 Purpose and Objective.....................................................................................................3 2.   Method Used to Collect Human Performance Data for W-22 Items 4  2.1 Recording System............................................................................................................5 2.2 Speech Material...............................................................................................................5 2.3 Background Noise...........................................................................................................6 2.4 Monaural and Binaural Test Recordings.........................................................................6 2.4.1 Monaural Recording............................................................................................6 2.4.2 Binaural Recording..............................................................................................6 2.5 Reverberation..................................................................................................................6 2.6 Human Performance Data...............................................................................................7 2.6.1 Participants..........................................................................................................7 2.6.2 Experimental Task...............................................................................................7 2.6.3 Counterbalancing of Stimuli ...............................................................................7 2.6.4 Apparati...............................................................................................................7 2.6.5 Test Data..............................................................................................................8 3.   Methods Used to Collect Human Performance Data on Callsign Acquisition Test (CAT) Items 9  3.1 Recordings.......................................................................................................................9 3.2 Human Performance Data...............................................................................................9 3.2.1 Participants..........................................................................................................9 3.2.2 Apparati.............................................................................................................10 3.2.3 Test Data............................................................................................................10 4.   Modeling Speech Intelligibility as Function of SNR 10   5.   Modeling Binaural Speech Intelligibility 13  
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