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Partial Mutual Information Based Input Variable Selection for Supervised Learning Approaches to Voice Activity Detection


Marković, Ivan; Jurić-Kavelj, Srećko; Petrović, Ivan
Partial Mutual Information Based Input Variable Selection for Supervised Learning Approaches to Voice Activity Detection // Applied soft computing, 13 (2013), 11; 4383-4391 doi:10.1016/j.asoc.2013.06.013 (međunarodna recenzija, članak, znanstveni)


Naslov
Partial Mutual Information Based Input Variable Selection for Supervised Learning Approaches to Voice Activity Detection

Autori
Marković, Ivan ; Jurić-Kavelj, Srećko ; Petrović, Ivan

Izvornik
Applied soft computing (1568-4946) 13 (2013), 11; 4383-4391

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Voice activity detection; partial mutual information; supervised learning; receiver operatingcharacteristics curves

Sažetak
The paper presents a novel approach for voice activity detection. The main idea behind the presented approach is to use, next to the likelihood ratio of a statistical model-based voice activity detector, a set of informative distinct features in order to, via a supervised learning approach, enhance the detection performance. The statistical model-based voice activity detector, which is chosen based on the comparison to other similar detectors in an earlier work, models the spectral envelope of the signal and we derive the likelihood ratio thereof. Furthermore, the likelihood ratio together with 70 other various features was meticulously analyzed with an input variable selection algorithm based on partial mutual information. The resulting analysis produced a 13 element reduced input vector which when compared to the full input vector did not undermine the detector performance. The evaluation is performed on a speech corpus consisting of recordings made by six different speakers, which were corrupted with three different types of noises and noise levels. In the end, we tested three different supervised learning algorithms for the task, namely, support vector machine, Boost, and artificial neural networks. The experimental analysis was performed by 10-fold cross-validation due to which threshold averaged receiver operating characteristics curves were constructed. Also, the area under the curve score and Matthew's correlation coefficient were calculated for both the three supervised learning classifiers and the statistical model-based voice activity detector. The results showed that the classifier with the reduced input vector significantly outperformed the standalone detector based on the likelihood ratio, and that among the three classifiers, Boost showed the most consistent performance.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti



POVEZANOST RADA


Projekt / tema
036-0363078-3018 - Upravljanje mobilnim robotima i vozilima u nepoznatim i dinamičkim okruženjima (Ivan Petrović, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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