Abstract
Numeral recognition is one amongst the foremost important problems in pattern recognition. Its numerous uses like reading communication postal code, worker code, bank cheque method etc. To the simplest of our information, very less work has been wiped out Marathi language as compared with other Indian and non-Indian languages. It has mentioned a unique technique for recognition of isolated Marathi numerals. It introduces Marathi numerals and identification technique using MFCC and DTW as attributes. The accuracy of the pre-recorded samples is greater than that of online testing samples. We have got additionally seen that the accuracy of the speaker dependent samples is over that of the speaker independent samples. Another technique known as HMM is additionally discussed. By experimentation, it’s ascertained that identification exactness is higher for HMM than DTW, but the training method in DTW is extremely straightforward and quick, as compared to HMM. The time needed for recognition of numerals using HMM is additional as compared to DTW, because it should bear the various states, iterations and lots of additional mathematical modeling, thus DTW is most well-liked for the real-time applications.
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References
Saon, G., Picheny, M.: Recent advances in conversational speech recognition using convolutional and recurrent neural networks. IBM J. Res. Dev. 61(4/5), 1 (2017)
O’Shaughnessy, D.: Automatic speech recognition. In: CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) (2015). https://doi.org/10.1109/chilecon.2015.7400411
Rabiner, L.R., Schafer, R.W.: Digital Processing Of Speech Signals, Low Price Edition. Pearson, London (2007)
Anusuya, M.A., Katti, S.K.: Speech recognition by machine: a review. (IJCSIS) Int. J. Comput. Sci. Inf. Secur. 6(3), 181–205 (2009)
Gawali, B.W., Gaikwad, S., Yannawar, P., Mehrotra, S.C.: Marathi isolated word recognition system using MFCC and DTW features. In: Proceedings of International Conference of Advances in Computer Science (2010)
Muda, L., Begam, M., Elamvazuth, I.: Voice recognition algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) techniques. J. Comput. 2(3) (2010). ISSN 2151-9617
Vimala, C., Radhab, V.: Speaker independent isolated speech recognition system for Tamil language using HMM. In: International Conference on Communication Technology and System Design (2011)
Bala, A., Kumar, A., Birla, N.: Voice command recognition system based on MFCC and DTW. Int. J. Eng. Sci. Technol. 2(12), 7335–7342 (2010)
Jiang, H., Li, X., Liu, C.: Large margin hidden Markov models for speech recognition. IEEE Trans. Audio Speech Lang. Process. 14(5) (2006)
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Gangonda, S.S., Patavardhan, P.P., Karande, K.J. (2019). Recognition of Marathi Numerals Using MFCC and DTW Features. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_17
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DOI: https://doi.org/10.1007/978-981-13-9187-3_17
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