Journal Article10.1016/J.COMPELECENG.2012.09.003
Voice activity detection algorithm using nonlinear spectral weights, hangover and hangbefore criteria
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TL;DR: A nonlinear function into the frequency spectrum that improves the detection of vowels, diphthongs, and semivowels within the speech signal and presents a procedure for faster definition of those optimal constants used by hangover and hangbefore criteria.
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About: This article is published in Computers & Electrical Engineering. The article was published on 01 Nov 2012. The article focuses on the topics: Voice activity detection.
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Citations
rVAD: An unsupervised segment-based robust voice activity detection method
TL;DR: A modified version of rVAD is presented where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation, which significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices.
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Recent Developments in Speech Enhancement in the Short-Time Fourier Transform Domain
TL;DR: An overview of the conventional literature in the single- and multichannel cases of noise reduction in the short-time Fourier transform (STFT) domain and a detailed survey of the most recent advances in the STFT-based noise reduction methods are provided.
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Formant-based robust voice activity detection
TL;DR: This paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise, which achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions.
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New automatic forward and backward blind sources separation algorithms for noise reduction and speech enhancement
Mohamed Djendi,Meriem Zoulikha +1 more
TL;DR: This paper proposes two new automatic VAD systems that allow adapting the original forward and backward BSS structures automatically, based on the use of the forward BSS structure to estimate the optimal values of the separating adaptive filters step-sizes.
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•Posted Content
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
TL;DR: In this article, an unsupervised segment-based method for robust voice activity detection (rVAD) is presented, which consists of two passes of denoising followed by a VAD stage, where high-energy segments in a speech signal are detected by using a posteriori signal-to-noise ratio (SNR) weighted energy difference.
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References
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The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
David Pearce,Hans-Günter Hirsch +1 more
- 01 Jan 2000
TL;DR: A database designed to evaluate the performance of speech recognition algorithms in noisy conditions and recognition results are presented for the first standard DSR feature extraction scheme that is based on a cepstral analysis.
A statistical model-based voice activity detection
TL;DR: An effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences is proposed which shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.
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Spectral Subtraction Based on Minimum Statistics
Rainer Martin
- 01 Jan 2001
TL;DR: An unbiased noise power estimator based on minimum statistics is derived and its statistical properties and its performance in the context of spectral subtraction are discussed.
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Robustness in Automatic Speech Recognition
Jean-Claude Junqua,Jean-Paul Haton +1 more
- 01 Jan 1996
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The voice activity detector for the Pan-European digital cellular mobile telephone service
Daniel Kenneth Freeman,G. Cosier,C.B. Southcott,Ivan Boyd +3 more
- 23 May 1989
TL;DR: In this article, a description of the voice activity detector (VAD) standardized by CEPT for use in the Pan-European digital cellular mobile telephone service is given, and performance tests carried out to validate the design are described.
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