Skip to main content

Speech Processing and Recognition System

  • Chapter
  • First Online:

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

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].

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Kamm, C., Walker, M., & Rabiner, L. (1997). The role of speech processing in human–computer intelligent communication. Speech Communication, 23(4), 263–278.

    Article  Google Scholar 

  2. Retrieved July 08, 2018, from https://www.sciencedirect.com/topics/neuroscience/speech-processing.

  3. 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.

    Google Scholar 

  4. Dey, N., & Ashour, A. S. (2018). Direction of arrival estimation and localization of multi-speech sources. Springer International Publishing.

    Google Scholar 

  5. 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.

    Google Scholar 

  6. 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.

    Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Mahendru, H. C. (2014). Quick review of human speech production mechanism. International Journal of Engineering Research and Development, 9(10), 48–54.

    Google Scholar 

  9. 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.

  10. Retrieved July 08, 2018, from https://www.youtube.com/watch?v=Xjzm7S__kBU.

  11. 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.

    Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Google Scholar 

  15. Retrieved July 20, 2018, from https://www.youtube.com/watch?v=q67z7PTGRi8&t=4294s.

  16. 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.

    Google Scholar 

  17. 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.

    Google Scholar 

  18. 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.

    Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. Retrieved July 20, 2018, from https://www.youtube.com/watch?v=kNloj1Qtf0Y&t=1500s.

  21. Gales, M., & Young, S. (2008). The application of hidden Markov models in speech recognition. Foundations and Trends® in Signal Processing, 1(3), 195–304.

    Article  Google Scholar 

  22. 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.

    Chapter  Google Scholar 

  23. 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.

    Chapter  Google Scholar 

  24. 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.

    Google Scholar 

  25. 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).

    Google Scholar 

  26. 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.

    Google Scholar 

  27. 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.

    Google Scholar 

  28. 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.

    Article  Google Scholar 

  29. 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.

    Google Scholar 

  30. 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.

    Google Scholar 

  31. Dey, N., Ashour, A. S., & Nguyen, G. N. Recent advancement in multimedia content using deep learning.

    Google Scholar 

  32. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533.

    Article  Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. 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.

    Google Scholar 

  35. Retrieved July 21, 2018, from https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a.

  36. Browman, C. P., & Goldstein, L. (1992). Articulatory phonology: An overview. Phonetica, 49(3–4), 155–180.

    Article  Google Scholar 

  37. Livescu, K., Jyothi, P., & Fosler-Lussier, E. (2016). Articulatory feature-based pronunciation modeling. Computer Speech & Language, 36, 212–232.

    Article  Google Scholar 

  38. Retrieved July 22, 2018, from http://www.speech.sri.com/projects/srilm/.

  39. Retrieved July 22, 2018, from https://kheafield.com/code/kenlm/.

  40. Chen, S. F., & Goodman, J. (1999). An empirical study of smoothing techniques for language modeling. Computer Speech & Language, 13(4), 359–394.

    Article  Google Scholar 

  41. Retrieved July 24, 2018, from https://www.slideshare.net/ssrdigvijay88/ngrams-smoothing.

  42. Retrieved July 24, 2018, from https://www.inf.ed.ac.uk/teaching/courses/asr/2011–12/asr-search-nup4.pdf.

  43. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269.

    Article  Google Scholar 

  44. 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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics