NDLI: A multifeature voiced/unvoiced decision algorithm for noisy speech

Content Provider	IEEE Xplore Digital Library
Author	Shahnaz, C. Zhu, W.-P. Ahmad, M.O.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que. (Shahnaz, C.; Zhu, W.-P.; Ahmad, M.O.)
Abstract	This paper presents a new algorithm for the voiced/unvoiced (V/UV) decision of noise-corrupted speech. A speech periodicity-harmonic function (SPHF) is proposed to manifest distinctive characteristics between voiced and unvoiced regions. A composite feature vector is developed by combining a periodicity measure obtained from the SPHF with some energy measures such as zero-crossing rate-weighted RMS energy, Kaiser-Teager frame energy and the normalized low-frequency energy ratio. Unlike the conventional hard threshold, a signal-dependent initial-threshold (SDIT) for each feature is determined based on its statistical properties. The SDIT is exploited to develop a logical expression that returns an objective score regarding V/UV region. Additional voicing criteria are introduced to remove the artifacts that may exist due to the overlapping between decision regions. Simulation results of the proposed multifeature classification scheme, using the Keele reference database, show superior efficacy at a low SNR relative to some of the existing V/UV decision algorithms
Sponsorship	The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS)
Starting Page	4
Ending Page	2528
File Size	386001
Page Count	2525
File Format	PDF
ISBN	0780393899
DOI	10.1109/ISCAS.2006.1693137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-21
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal processing algorithms Speech synthesis Speech analysis Pollution measurement Low-frequency noise Speech enhancement Energy measurement Gaussian noise Speech coding Autocorrelation
Content Type	Text
Resource Type	Article

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