NDLI: Detecting bandlimited audio in broadcast television shows

Content Provider	IEEE Xplore Digital Library
Author	Fuhs, M.C. Qin Jin Schultz, T.
Copyright Year	2009
Description	Author affiliation: InterACT, Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA (Fuhs, M.C.; Qin Jin; Schultz, T.)
Abstract	For TV and radio shows containing narrowband speech, Speech-to-text (STT) accuracy on the narrowband audio can be improved by using an acoustic model trained on acoustically matched data. To selectively apply it, one must first be able to accurately detect which audio segments are narrowband. The present paper explores two different bandwidth classi??cation approaches: a traditional Gaussian mixture model (GMM) approach and a spline-based classifier that categorizes audio segments based on their power spectra. We focus on shows found in the DARPA GALE Mandarin training and test sets, where the ratio of wideband to narrowband shows is very large. In this setting, the spline-based classifier reduces the number of misclassified wideband segments by up to 95% relative to the GMM-based classi??er for the same number of misclassified narrowband segments.
Starting Page	4589
Ending Page	4592
File Size	252257
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4960652
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	TV broadcasting Narrowband Wideband Acoustic signal detection Bandwidth Speech Acoustic testing Spline Telephony Decoding telephony Speech processing speech recognition pattern classification
Content Type	Text
Resource Type	Article

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