Abstract
Accent identification is one of the applications paid more attention in speech processing. A text-independent accent identification system is proposed using Gaussian mixture models (GMMs) for Kannada language. Spectral and prosodic features such as Mel-frequency cepstral coefficients (MFCCs), pitch, and energy are considered for the experimentation. The dataset is collected from three regions of Karnataka namely Mumbai Karnataka, Mysore Karnataka, and Karavali Karnataka having significant variations in accent. Experiments are conducted using 32 speech samples from each region where each clip is of one minute duration spoken by native speakers. The baseline system implemented using MFCC features found to achieve 76.7 % accuracy. From the results it is observed that the hybrid features improve the performance of the system by 3 %.
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Soorajkumar, R., Girish, G.N., Ramteke, P.B., Joshi, S.S., Koolagudi, S.G. (2017). Text-Independent Automatic Accent Identification System for Kannada Language. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_40
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DOI: https://doi.org/10.1007/978-981-10-1678-3_40
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