Abstract
Today’s advancement in the research field has brought a new horizon to design the state-of-the-art systems that produce sound utterance. In order to attain a higher level of speech understanding potentiality, it is of utmost importance to achieve good efficiency. Speech-to-Text (STT) or voice recognition system is an efficacious approach that aims at recognizing speech and allows the conversion of the human voice into the text. By this, an interface between the human and the computer is created. In this direction, this paper introduces a novel approach to convert STT by using Hidden Markov Model (HMM). HMM along with other techniques such as Mel-Frequency Cepstral Coefficients (MFCCs), Decision trees, Support Vector Machine (SVM) is used to ascertain the speakers’ utterances and catalyse these utterances into quantization features by evaluating the likelihood extremity of the spoken word. The accuracy of the proposed architecture is studied, which is found to be better than the existing methodologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alias, F., et al.: Towards high-quality next generation text-to-speech synthesis: a multi domain approach by automatic domain classification. IEEE Trans. Audio Speech Lang. Process. 16(7) (Sept 2008)
Abushariah, A.A.M., et al.: English digits speech recognition system based on Hidden Markov Models. In: IEEE Conference 2010, ICCCE. doi:10.1109/ICCCE.2010.5556819
Hossan, M.A., et al.: A novel approach for MFCC feature extraction. In: IEEE Conference 2010, ICSPCS. doi:10.1109/ICSPCS.2010.5709752
Bsyrne, W.: Minimum Bayes risk estimation and decoding in large vocabulary continuous speech recognition. IEEE E89-D(3), 900–907 (2006)
Patel, I., et al.: Speech recognition using hidden Markov model with MFCC-subband technique. In: IEEE Conference (2010). doi:10.1109/ITC.2010.45
Duan, W., et al.: Weighted naive Bayesian classifier model based on information gain. In: IEEE Conference, (ISDEA). doi:10.1109/ISDEA.2010.226
Gales, M., et al.: The application of hidden Markov models in speech recognition. Found. Trends Signal Process. 1(3), 195–304 (2008)
Swamy, S., et al.: An efficient speech recognition system. Comput. Sci. Eng. Int. J. (CSEIJ) 3(4) (Aug 2013)
Kholghi, M., et al.: Classification and evaluation of data mining techniques for data stream requirements. In: IEEE Conference on Computer Communication Control and Automation (3CA). doi:10.1109/3CA.2010.5533759
Shahrokhi, N., et al.: Targeting customers with data mining techniques: classification. In: 2011 International Conference on User Science and Engineering (i-USEr), IEEE, New York. doi:10.1109/iUSEr.2011.6150567
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rashmi, S., Hanumanthappa, M., Reddy, M.V. (2018). Hidden Markov Model for Speech Recognition System—A Pilot Study and a Naive Approach for Speech-To-Text Model. In: Agrawal, S., Devi, A., Wason, R., Bansal, P. (eds) Speech and Language Processing for Human-Machine Communications. Advances in Intelligent Systems and Computing, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-6626-9_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-6626-9_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6625-2
Online ISBN: 978-981-10-6626-9
eBook Packages: EngineeringEngineering (R0)