A Novel K-Means Voice Activity Detection Algorithm Using Linear Cross Correlation on the Standard Deviation of Linear Predictive Coding

Author:

Mustafa M. K.,Allen Tony,Appiah Kofi

Publisher

Springer International Publishing

Reference14 articles.

1. Wu, B., Wang, K.: Voice activity detection based on auto-correlation function using wavelet transform and teager energy operator. Comput. Linguist. Chin. Lang. Process. 11, 87–100 (2006)

2. Waheed, K., Weaver, K., Salam, F.M.: A robust algorithm for detecting speech segments using an entropic contrast: circuits and systems. In: 2002. MWSCAS-2002. The 2002 45th Midwest Symposium on IEEE, vol. 3, pp. III-328–III-331 (2002)

3. Alarifi, A., Alkurtass, I., Al-Salman, A.: Arabic text-dependent speaker verification for mobile devices using artificial neural networks. In: Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on, vol. 2, pp. 350–353, IEEE (2011)

4. Huang, H., Lin, F.: A speech feature extraction method using complexity measure for voice activity detection in WGN. Speech Commun. 51, 714–723 (2009)

5. Ghaemmaghami, H., Baker, B.J., Vogt, R.J., Sridharan, S.: Noise robust voice activity detection using features extracted from the time-domain autocorrelation function. In: Proceedings of Interspeech (2010)

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