Abstract
In the initial decade of the twentieth century, scientists in the Bell System realized that the idea of universal services like telephony services is becoming feasible due to large-scale technological revolution [1].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kamm, C., Walker, M., & Rabiner, L. (1997). The role of speech processing in human–computer intelligent communication. Speech Communication, 23(4), 263–278.
Retrieved July 08, 2018, from https://www.sciencedirect.com/topics/neuroscience/speech-processing.
Dey, N., & Ashour, A. S. (2018). Challenges and future perspectives in speech-sources direction of arrival estimation and localization. In Direction of arrival estimation and localization of multi-speech sources (pp. 49–52). Cham: Springer.
Dey, N., & Ashour, A. S. (2018). Direction of arrival estimation and localization of multi-speech sources. Springer International Publishing.
Dey, N., & Ashour, A. S. (2018). Applied examples and applications of localization and tracking problem of multiple speech sources. In Direction of arrival estimation and localization of multi-speech sources (pp. 35–48). Cham: Springer.
Dey, N., & Ashour, A. S. (2018). Microphone array principles. In Direction of arrival estimation and localization of multi-speech sources (pp. 5–22). Cham: Springer.
Kamal, M. S., Chowdhury, L., Khan, M. I., Ashour, A. S., Tavares, J. M. R., & Dey, N. (2017). Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images. Computational Biology and Chemistry, 68, 231–244.
Mahendru, H. C. (2014). Quick review of human speech production mechanism. International Journal of Engineering Research and Development, 9(10), 48–54.
Shirodkar, N. S. (2016). Konkani Speech to Text Recognition using Hidden MARKOV Model Toolit (Masters dissertation). Retrieved July 08, 2018, from https://www.kom.aau.dk/group/04gr742/pdf/speech_production.pdf.
Retrieved July 08, 2018, from https://www.youtube.com/watch?v=Xjzm7S__kBU.
Sood, S., & Krishnamurthy, A. (2004, October). A robust on-the-fly pitch (OTFP) estimation algorithm. In Proceedings of the 12th Annual ACM International Conference on Multimedia (pp. 280–283). ACM.
De Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917–1930.
Chowdhury, S., Datta, A. K., & Chaudhuri, B. B. (2000). Pitch detection algorithm using state phase analysis. J Acoust Soc India, 28(1–4), 247–250.
Yu, Y. (2012, March). Research on speech recognition technology and its application. In 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), (Vol. 1, pp. 306–309). IEEE.
Retrieved July 20, 2018, from https://www.youtube.com/watch?v=q67z7PTGRi8&t=4294s.
Dey, N., Ashour, A. S., Mohamed, W. S., & Nguyen, N. G. (2019). Acoustic wave technology. In Acoustic sensors for biomedical applications (pp. 21–31). Cham: Springer.
Dey, N., Ashour, A. S., Mohamed, W. S., & Nguyen, N. G. (2019). Acoustic sensors in biomedical applications. In Acoustic sensors for biomedical applications (pp. 43–47). Cham: Springer.
Khiatani, D., & Ghose, U. (2017, October). Weather forecasting using hidden Markov model. In 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), (pp. 220–225). IEEE.
Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech synthesis based on hidden Markov models. Proceedings of the IEEE, 101(5), 1234–1252.
Retrieved July 20, 2018, from https://www.youtube.com/watch?v=kNloj1Qtf0Y&t=1500s.
Gales, M., & Young, S. (2008). The application of hidden Markov models in speech recognition. Foundations and Trends® in Signal Processing, 1(3), 195–304.
Rabiner, L. R., & Juang, B. H. (1992). Hidden Markov models for speech recognition—strengths and limitations. In Speech recognition and understanding (pp. 3–29). Heidelberg: Springer.
Hore, S., Bhattacharya, T., Dey, N., Hassanien, A. E., Banerjee, A., & Chaudhuri, S. B. (2016). A real time dactylology based feature extraction for selective image encryption and artificial neural network. In Image feature detectors and descriptors (pp. 203–226). Cham: Springer.
Samanta, S., Kundu, D., Chakraborty, S., Dey, N., Gaber, T., Hassanien, A. E., & Kim, T. H. (2015, September). Wooden Surface classification based on Haralick and the Neural Networks. In 2015 Fourth International Conference on Information Science and Industrial Applications (ISI), (pp. 33–39). IEEE.
Kotyk, T., Ashour, A. S., Chakraborty, S., Dey, N., & Balas, V. E. (2015). Apoptosis analysis in classification paradigm: a neural network based approach. In Healthy World Conference (pp. 17–22).
Agrawal, S., Singh, B., Kumar, R., & Dey, N. (2019). Machine learning for medical diagnosis: A neural network classifier optimized via the directed bee colony optimization algorithm. In U-Healthcare monitoring systems (pp. 197–215). Academic Press.
Wang, Y., Chen, Y., Yang, N., Zheng, L., Dey, N., Ashour, A. S., … & Shi, F. (2018). Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Applied Soft Computing.
Lan, K., Wang, D. T., Fong, S., Liu, L. S., Wong, K. K., & Dey, N. (2018). A survey of data mining and deep learning in bioinformatics. Journal of Medical Systems, 42(8), 139.
Hu, S., Liu, M., Fong, S., Song, W., Dey, N., & Wong, R. (2018). Forecasting China future MNP by deep learning. In Behavior engineering and applications (pp. 169–210). Cham: Springer.
Dey, N., Fong, S., Song, W., & Cho, K. (2017, August). Forecasting energy consumption from smart home sensor network by deep learning. In International Conference on Smart Trends for Information Technology and Computer Communications (pp. 255–265). Singapore: Springer.
Dey, N., Ashour, A. S., & Nguyen, G. N. Recent advancement in multimedia content using deep learning.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533.
Mohamed, A. R., Dahl, G. E., & Hinton, G. (2012). Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech & Language Processing, 20(1), 14–22.
Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6645–6649). IEEE.
Retrieved July 21, 2018, from https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a.
Browman, C. P., & Goldstein, L. (1992). Articulatory phonology: An overview. Phonetica, 49(3–4), 155–180.
Livescu, K., Jyothi, P., & Fosler-Lussier, E. (2016). Articulatory feature-based pronunciation modeling. Computer Speech & Language, 36, 212–232.
Retrieved July 22, 2018, from http://www.speech.sri.com/projects/srilm/.
Retrieved July 22, 2018, from https://kheafield.com/code/kenlm/.
Chen, S. F., & Goodman, J. (1999). An empirical study of smoothing techniques for language modeling. Computer Speech & Language, 13(4), 359–394.
Retrieved July 24, 2018, from https://www.slideshare.net/ssrdigvijay88/ngrams-smoothing.
Retrieved July 24, 2018, from https://www.inf.ed.ac.uk/teaching/courses/asr/2011–12/asr-search-nup4.pdf.
Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269.
Gerber, M., Kaufmann, T., & Pfister, B. (2011, May). Extended Viterbi algorithm for optimized word HMMs. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4932–4935). IEEE.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sen, S., Dutta, A., Dey, N. (2019). Speech Processing and Recognition System. In: Audio Processing and Speech Recognition. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6098-5_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-6098-5_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6097-8
Online ISBN: 978-981-13-6098-5
eBook Packages: EngineeringEngineering (R0)