Abstract
Digit recognition is one of the elegant research topics in modern world. Scientists had already got an excellent output in their research work on this topic for English and Chinese like languages. However, very few works exist on digit recognition for regional language. Moreover in case of regional language the pronunciation rapidly varies on the basis of area and the performance of their proposed recognition reasonably low. In this paper we have worked on digit recognition on a regional language, Bengali referred to as Bangla. In our proposed work of isolated digit and word recognition, we created a small speech database, containing ten Bangla digit zero to nine (pronounced as ‘sunno’ to ‘noi’) and four Bangla words
(English equivalent Right, Left, Above, and Below respectively) with 100 samples for each class. We have done a pre-processing phase, followed by a 39 dimensional feature extraction procedure of Mel Frequency Cepstral Coefficients (MFCC), \(\varDelta \) MFCC and \(\varDelta \) \(\varDelta \) MFCC. Finally, we used the Dynamic Time Warping (DTW) for classification of testing purpose. The system has achieved a highest accuracy of 93% for classification of Bangla words and digits, which is considerably satisfactory. The comparative analysis with the existing method is given in Sect. 5.
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Paul, B., Paul, R., Bera, S., Phadikar, S. (2023). Isolated Bangla Spoken Digit and Word Recognition Using MFCC and DTW. In: Gyei-Kark, P., Jana, D.K., Panja, P., Abd Wahab, M.H. (eds) Engineering Mathematics and Computing. Studies in Computational Intelligence, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-19-2300-5_16
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