Skip to main content
SpringerLink
Log in

Automatic speech segmentation in syllable centric speech recognition system

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

Speech recognition is the process of understanding the human or natural language speech by a computer. A syllable centric speech recognition system in this aspect identifies the syllable boundaries in the input speech and converts it into the respective written scripts or text units. Appropriate segmentation of the acoustic speech signal into syllabic units is an important task for development of highly accurate speech recognition system. This paper presents an automatic syllable based segmentation technique for segmenting continuous speech signals in Indian languages at syllable boundaries. To analyze the performance of the proposed technique, a set of experiments are carried out on different speech samples in three Indian languages Hindi, Bengali and Odia and are compared with the existing group delay based segmentation technique along with the manual segmentation technique. The results of all our experiments show the effectiveness of the proposed technique in segmenting the syllable units from the original speech samples compared to the existing techniques.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Besacier, L., Barnard, E., Karpov, A., & Schultz, T. (2014). Automatic speech recognition for under-resourced languages: A survey. Speech Communication, 56, 85–100.

    Article  Google Scholar 

  • Gałka, J., Masior, M., & Salasa, M. (2014). Voice authentication embedded solution for secured access control. IEEE Transactions on Consumer Electronics, 60(4), 653–661.

    Article  Google Scholar 

  • He, Y., Han, J., Zheng, T., & Sun, G. (2014). A new framework for robust speech recognition in complex channel environments. Digital Signal Processing, 32, 109–123.

    Article  Google Scholar 

  • Kay, S. M., & Sudhaker, R. (1986). A zero crossing-based spectrum analyzer. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(1), 96–104.

    Article  Google Scholar 

  • Kelly, F., Drygajlo, A., & Harte, N. (2013). Speaker verification in score-ageing-quality classification space. Computer Speech & Language, 27(5), 1068–1084.

    Article  Google Scholar 

  • Kitaoka, N., Enami, D., & Nakagawa, S. (2014). Effect of acoustic and linguistic contexts on human and machine speech recognition. Computer Speech & Language, 28(3), 769–787.

    Article  Google Scholar 

  • Koolagudi, S. G., & Rao, K. S. (2012). Emotion recognition from speech using source, system, and prosodic features. International Journal of Speech Technology, 15(2), 265–289.

    Article  Google Scholar 

  • Lau, Y. K., & Chan, C. K. (1985). Speech recognition based on zero crossing rate and energy. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(1), 320–323.

    Article  Google Scholar 

  • Li, M., Han, K. J., & Narayanan, S. (2013). Automatic speaker age and gender recognition using acoustic and prosodic level information fusion. Computer Speech & Language, 27(1), 151–167.

    Article  Google Scholar 

  • Lin, C. H., Wu, C. H., Ting, P. Y., & Wang, H. M. (1996). Frameworks for recognition of Mandarin syllables with tones using sub-syllabic units. Speech Communication, 18(2), 175–190.

    Article  Google Scholar 

  • Lippmann, R. P. (1997). Speech recognition by machines and humans. Speech Communication, 22(1), 1–15.

    Article  Google Scholar 

  • Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014). Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks. IEEE Transactions on Multimedia, 16(8), 2203–2213.

    Article  Google Scholar 

  • McLoughlin, I. V. (2014). Super-audible voice activity detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(9), 1424–1433.

    Article  Google Scholar 

  • Musfir, M., Krishnan, K. R., & Murthy, H. (2014). Analysis of fricatives, stop consonants and nasals in the automatic segmentation of speech using the group delay algorithm. In Twentieth National Conference on Communications (NCC) (pp. 1–6).

  • Obin, N., Lamare, F., & Roebel, A. (2013). Syll-O-Matic: an adaptive time-frequency representation for the automatic segmentation of speech into syllables. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. 6699–6703).

  • Origlia, A., Cutugno, F., & Galatà, V. (2014). Continuous emotion recognition with phonetic syllables. Speech Communication, 57, 155–169.

    Article  Google Scholar 

  • Panda, S. P., & Nayak, A. K. (2015). An efficient model for text-to-speech synthesis in Indian languages. International Journal of Speech Technology, 18(3), 305–315.

    Article  Google Scholar 

  • Panda, S. P., Nayak, A. K., & Patnaik, S. (2015). Text-to-speech synthesis with an Indian language perspective. International Journal of Grid and Utility Computing, 6(3–4), 170–178.

    Article  Google Scholar 

  • Prasad, V. K., Nagarajan, T., & Murthy, H. A. (2004). Automatic segmentation of continuous speech using minimum phase group delay functions. Speech Communication, 42(3), 429–446.

    Article  Google Scholar 

  • Prasanna, S., Reddy, B. V. S., & Krishnamoorthy, P. (2009). Vowel onset point detection using source, spectral peaks, and modulation spectrum energies. IEEE Transactions on Audio, Speech, and Language Processing, 17(4), 556–565.

    Article  Google Scholar 

  • Sakai, T., & Doshita, S. (1963). The automatic speech recognition system for conversational sound. IEEE Transactions on Electronic Computers, 6, 835–846.

    Article  Google Scholar 

  • Shastri, L., Chang, S., & Greenberg, S. (1999). Syllable detection and segmentation using temporal flow neural networks. In International Congress of Phonetic Sciences (pp. 1721–1724).

  • Sirigos, J., Fakotakis, N., & Kokkinakis, G. (2002). A hybrid syllable recognition system based on vowel spotting. Speech Communication, 38(3), 427–440.

    Article  MATH  Google Scholar 

  • Sreenivas, T. V., & Niederjohn, R. J. (1992). Zero-crossing based spectral analysis and SVD spectral analysis for formant frequency estimation in noise. IEEE Transactions on Signal Processing, 40(2), 282–293.

    Article  Google Scholar 

  • Wang, H. M. (2000). Experiments in syllable-based retrieval of broadcast news speech in Mandarin Chinese. Speech Communication, 32(1), 49–60.

    Article  Google Scholar 

  • Wang, G., & Sim, K. C. (2014). Regression-based context-dependent modeling of deep neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(11), 1660–1669.

    Article  Google Scholar 

  • Zhao, X., & Shaughnessy, D. O. (2008). A new hybrid approach for automatic speech signal segmentation using silence signal detection, energy convex hull, and spectral variation. In Canadian Conference on Electrical and Computer Engineering (pp. 145–148).

  • Ziolko, B., Manandhar, S., Wilson, R. C., & Ziolko, M. (2006). Wavelet method of speech segmentation. In 14th European Signal Processing Conference (pp. 1–5).

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Soumya Priyadarsini Panda

  2. Department of CS&IT, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Ajit Kumar Nayak

Authors
  1. Soumya Priyadarsini Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajit Kumar Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumya Priyadarsini Panda.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panda, S.P., Nayak, A.K. Automatic speech segmentation in syllable centric speech recognition system. Int J Speech Technol 19, 9–18 (2016). https://doi.org/10.1007/s10772-015-9320-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-015-9320-6

Keywords

Search

Navigation