NDLI: Feature analysis for quality assessment of reverberated speech

Content Provider	IEEE Xplore Digital Library
Author	de Lima, A.A. de Prego, T.M. Netto, S.L. Lee, B. Said, A. Schafer, R.W. Kalker, T. Fozunbal, M.
Copyright Year	2009
Description	Author affiliation: PEE/COPPE, Federal University of Rio de Janeiro, Brazil (de Lima, A.A.; de Prego, T.M.; Netto, S.L.) \|\| HP Labs - Palo Alto, USA (Lee, B.; Said, A.; Schafer, R.W.; Kalker, T.; Fozunbal, M.)
Abstract	This paper analyzes the ability of several measurements to quantify the reverberation effect in speech signals. We consider an intrusive scheme, in which the clean and reverberated signals are available, allowing one to estimate the corresponding room impulse response (RIR) signal. An artificial neural network (ANN) is trained for all features and used in a regression approach to estimate the human perceptual evaluation in a mean opinion score (MOS) 1–5 scale. Dimensionality reduction approaches are applied to generate a simpler ANN regression, establishing the most representative features for the problem at hand. A correlation level of 85% with subjective test scores was achieved by reducing the input-vector dimension from 10 to 3, including only the features of reverberation time, room spectral variance, and direct-to-reverberant energy ratio.
Starting Page	1
Ending Page	5
File Size	184348
Page Count	5
File Format	PDF
ISBN	9781424444632
DOI	10.1109/MMSP.2009.5293326
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-05
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech analysis Quality assessment Reverberation Artificial neural networks Acoustic reflection Signal analysis Testing Performance analysis Humans Convolution
Content Type	Text
Resource Type	Article

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