Abstract
This paper presents a novel Voice Activity Detection (VAD) technique that can be easily applied to on–device isolated word recognition on a mobile device. The main speech features used are the Linear Predictive Coding (LPC) speech features which were correlated using the standard deviation of the signal. The output was further clustered using a modified K-means algorithm. The results presented show a significant improvement to a previous algorithm which was based on the LPC residual signal with an 86.6 % recognition rate as compared to this new technique with a 90 % recognition rate on the same data. This technique was able to achieve up to 97.7 % recognition for female users in some of the experiments. The fast processing time makes it viable for mobile devices.
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Acknowledgements
The authors wish to thank the Petroleum Technology Development Fund (PTDF) for their continued support and sponsorship of this research. Dr. S Mustafa, Aishatu Mustafa and colleagues who helped in conducting experiments.
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Mustafa, M.K., Allen, T., Appiah, K. (2015). A Novel K-Means Voice Activity Detection Algorithm Using Linear Cross Correlation on the Standard Deviation of Linear Predictive Coding. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_24
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DOI: https://doi.org/10.1007/978-3-319-25032-8_24
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