The International Arab Journal of Information Technology (IAJIT)


Recognition of Spoken Bengali Numerals Using

This paper presents a method of automatic recognition of Bengali numerals spoken in noise-free and noisy environments by multiple speakers with different dialects. Mel Frequency Cepstral Coefficients (MFCC) are used for feature extraction, and Principal Component Analysis is used as a feature summarizer to form the feature vector from the MFCC data for each digit utterance. Finally, we use Support Vector Machines, Multi-Layer Perceptrons, and Random Forests to recognize the Bengali digits and compare their performance. In our approach, we treat each digit utterance as a single indivisible entity, and we attempt to recognize it using features of the digit utterance as a whole. This approach can therefore be easily applied to spoken digit recognition tasks for other languages as well.

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[19] Zheng F., Zhang G., and Song Z., Comparison of Different Implementations of MFCC, Journal of Computer Science and Technology, vol. 16, no. 6, pp. 582-589, 2001. Avisek Gupta He has obtained an M.E. degree from Jadavpur University, Kolkata, India, and has previously obtained a B.Tech. Degree from Future Institute of Engineering and Management, Kolkata, India. His research interests include Speech Recognition, Information Retrieval, and Machine Learning. Kamal Sarkar He received his B.E degree in Computer Science and Engineering from the Faculty of Engineering, Jadavpur University in 1996. He received the M.E degree and Ph.D. (Engg) in Computer Science and Engg. From the same University in 1999 and 2011 respectively. In 2001, he joined as a lecturer in the Department of Computer Science & Engineering, Jadavpur University, Kolkata, where he is currently a professor. His research interest includes Natural Language Processing, Machine Learning, Text Summarization, Text Mining, Speech Recognition.