Abstract
This paper presents a comparative evaluation of different methods for voice activity detection (VAD). A novel feature set is proposed in order to improve VAD performance in diverse noisy environments. Furthermore, three classifiers for VAD are evaluated. The three classifiers are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Decision Tree (DT). Experimental results show that the proposed feature set achieves better performance than spectral entropy. In the comparison of the classifiers, DT shows the best performance in terms of frame-based VAD accuracy as well as computational cost.
| Original language | English |
|---|---|
| Pages (from-to) | 107-110 |
| Number of pages | 4 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Publication status | Published - 2008 |
| Event | INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia Duration: 2008 Sept 22 → 2008 Sept 26 |
Keywords
- Decision Tree (DT)
- Gaussian Mixture Model (GMM)
- Support Vector Machine (SVM)
- Voice activity detection (VAD)