Biologically Based Non-Language Speech Sound Detection

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-08-C-0119
Agency Tracking Number: F071-079-1624
Amount: $749,380.00
Phase: Phase II
Program: SBIR
Awards Year: 2008
Solicitation Year: 2007
Solicitation Topic Code: AF071-079
Solicitation Number: 2007.1
Small Business Information
425 Oser Avenue, Hauppauge, NY, 11788
DUNS: 606421105
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Bruce Stewart
 Senior Systems Engineer
 (631) 273-5700
Business Contact
 Richard Lawless
Title: Vice President Operations
Phone: (631) 273-5700
Research Institution
This proposal addresses the application of new acoustic processing technologies to automatically identify and eliminate non-language speech sounds as a pre-processing stage to improve audio processing. Non-language speech sounds (coughing, breathing, ‘ah,’ ‘uhmm’) make up a large part of natural human language use, but contemporary speech recognition data preparation relies on hand-labeling of non-language speech sounds. The proposed work will extend, improve, and refine the capabilities of a computational model of auditory cortical processing based on multiscale spectro-temporal modulation features to the automated detection of non-language speech sounds. Advanced Acoustic Concepts and the University of Maryland have extensive experience applying the computational model to a variety of speech processing problems. Phase I results indicate that cortical processing algorithms are highly capable of identifying non-language speech sounds. Recognition algorithms will be trained and tested to distinguish non-language speech sounds collectively and individually, to classify them by individual type, and to determine accurate segmentation of the sound stream. Speech enhancement algorithms based on the same cortical processing model will be designed or adapted and applied to improve the identification of non-language speech sounds under noisy conditions. The feasibility of performing identification, classification, and segmentation in near real time will be demonstrated.

* Information listed above is at the time of submission. *

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