Noise robust voice activity detection using features extracted from the time-domain autocorrelation function
Ghaemmaghami, Houman, Baker, Brendan J., Vogt, Robert J., & Sridharan, Sridha (2010) Noise robust voice activity detection using features extracted from the time-domain autocorrelation function. In Proceedings of Interspeech 2010, Makuhari Messe International Convention Complex, Makuhari, Japan.
This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.
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|Item Type:||Conference Paper|
|Keywords:||Voice Activity Detection, High Noise Autocorrelation, Zero-crossing Rate, Time-domain Analysis|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2010 [please consult the authors]|
|Deposited On:||29 Mar 2011 07:43|
|Last Modified:||01 Mar 2012 10:59|
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