Spectro-temporal directional derivative based automatic speech recognition for a serious game scenario

Multimedia Tools and Applications - Tập 74 - Trang 5313-5327 - 2014
Ghulam Muhammad1, Mehedi Masud2, Abdulhameed Alelaiwi3, Md. Abdur Rahman4, Ali Karime5, Atif Alamri6, M. Shamim Hossain3
1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
2Department of Computer Science, Taif University, Taif, Saudi Arabia
3Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
4Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
5Multimedia Communications Research Laboratory, University of Ottawa, Ottawa, Canada
6Department of Information System, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Tóm tắt

Speech is one of the important modalities in a serious game platform. Serious game can be very useful for the rehabilitation of individuals with voice disorders. Therefore, we need an efficient and high-performance automatic speech recognition (ASR) system. In this paper, we propose a spectro-temporal directional derivative (STDD) feature that requires less number of computations in the modeling and yet gives high recognition accuracy in the ASR system. The proposed STDD feature is achieved by applying different directional derivative filters in the spectro-temporal domain. The feature dimension is then compressed by discrete cosine transform. The experiments are performed with voice samples of Arabic numerals spoken by persons with and without voice pathology. The experimental results show that the STDD feature outperforms the conventional mel-frequency cepstral coefficients both in clean and noisy environments.

Tài liệu tham khảo

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Multimedia Tools and Applications

Tập 74

5313-5327

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