Abstract
Speech is essential to exchange information. Speech recognition is one of the interfaces for man-machine interaction. However, the performance of these systems is restricted to noisy acoustic conditions. Silent speech i.e. visual dynamic features of speech have more potential information for Human-Computer Interaction. This paper presents lip localization and segmentation by Otsu algorithm. The height and width parameters of lip movements are captured as visual cues for silent speech recognition. We develop stochastic visual word models with an in-house database of 20 subjects. Performance evaluation these models are measured by word error rate. The accuracy of the system recorded for speaker dependent female subjects is 84.6%, and 65.8% as an overall result.
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Kandagal, A.P., Udayashankara, V., Anusuya, M.A. (2018). Silent Speech Recognition. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_13
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DOI: https://doi.org/10.1007/978-981-10-9059-2_13
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