Academic Commons

Articles

Computational auditory scene analysis exploiting speech-recognition knowledge

Ellis, Daniel P. W.

The field of computational auditory scene analysis (CASA) strives to build computer models of the human ability to interpret sound mixtures as the combination of distinct sources. A major obstacle to this enterprise is defining and incorporating the kind of high level knowledge of real-world signal structure exploited by listeners. Speech recognition, while typically ignoring the problem of nonspeech inclusions, has been very successful at deriving powerful statistical models of speech structure from training data. In this paper, we describe a scene analysis system that includes both speech and nonspeech components, addressing the problem of working backwards from speech recognizer output to estimate the speech component of a mixture. Ultimately, such hybrid approaches will require more radical adaptation of current speech recognition approaches.

Files

Also Published In

Title
1997 IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics: October 19-22, Mohonk Mountain House, New Paltz, New York
DOI
https://doi.org/10.1109/ASPAA.1997.625625

More About This Work

Academic Units
Electrical Engineering
Publisher
IEEE
Published Here
July 3, 2012
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.