Skip to main content
SpringerLink
Log in

Prosodic word boundary detection from Bengali continuous speech

  • Original Paper
  • Published:
Language Resources and Evaluation Aims and scope Submit manuscript

Abstract

Detection of word boundaries in continuous speech is a tedious process due to the absence of a definite pause or silence in the word boundary position. Thus, continuous speech recognition is a very challenging task. However, the prosodic word boundaries, unlike the written word boundaries, can be predicted using the prosodic parameters of continuous speech. This paper proposes a method for detecting such prosodic word boundaries from Bengali continuous speech. Bengali is a bound-stress language, where stress is observed on the first syllable of a prosodic word. Empirical Mode Decomposition is applied to the logarithm of fundamental frequency (F0) contour of continuous speech to detect prosodic word boundaries. 200 Bengali readout sentences, read by ten speakers, are analyzed for the present work. An overall prosodic boundary detection accuracy of 88.05% is achieved, whereas precision and recall values are 90.73% and 88.31%, respectively, with f-score as 89.5. A prosodic word dictionary comprising 5031 prosodic words has been developed by analyzing 1526 Bengali sentences with the proposed prosodic word boundary detection method.

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

Similar content being viewed by others

References

  • Acharya, S., & Das Mandal, S. K. (2013). Prosodic word and phrase boundary detection based on F0 contour analysis using empirical mode decomposition. In Oriental COCOSDA/CASLRE (pp. 1–5). IEEE.

  • Agarwal, A., Jain, A., Prakash, N., & Agarwal, S. (2010). Word boundary detection in continuous speech based on supra segmental features for Hindi Language. In 2nd International Conference on Signal Processing Systems (pp. 591–594). Dalian: IEEE.

  • Alam, F., Murtoza Habib, S., Sultana, A., & Khan, M. (2010). Development of annotated bangla speech corpora. In Spoken Languages Technologies for Under-Resourced Languages (pp. 35–41).

  • Ananthakrishnan, S., & Narayanan, S. (2007). Improved speech recognition using acoustic and lexical correlates of pitch accent in a n-best rescoring framework. In International Conference on Acoustics, Speech and Signal Processing—ICASSP’07 (pp. 873–876). Honolulu: IEEE.

  • Ananthakrishnan, S., & Narayanan, S. (2009). Unsupervised adaptation of categorical prosody models for prosody labeling and speech recognition. IEEE Transactions on Audio, Speech and Language Processing,17(1), 138–149.

    Article  Google Scholar 

  • Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4), 555–596.

    Article  Google Scholar 

  • Bhowmik, T. (2017). Prosodic and Phonological Feature based Speech Recognition System for Bengali, PhD Thesis, IIT Kharagpur.

  • Bhowmik, T., & Das Mandal, S. K. (2018). Manner of articulation based Bengali phoneme classification. International Journal of Speech Technology,21(2), 233–250.

    Article  Google Scholar 

  • Boersma, P., & Weenink, D. (2016). Praat: Doing phonetics by computer.[computer program]. Version 6.0.19. Retrieved 2016, from http://www.praat.org.

  • Campbell, N. (1993). Automatic detection of prosodic boundaries in speech. Speech Communication,13(3–4), 343–354.

    Article  Google Scholar 

  • Campbell, N., & Black, A. (1997). Prosody and the selection of source units for concatenative synthesis. Progress in speech synthesis (pp. 279–292). New York: Springer.

    Chapter  Google Scholar 

  • Chen, S.-H., Yang, J.-H., Chiang, C.-Y., Liu, M.-C., & Wang, Y.-R. (2012). A new prosody-assisted mandarin ASR system. IEEE Transactions on Audio, Speech and Language Processing,20(6), 1669–1684.

    Article  Google Scholar 

  • Das Mandal, S. K. (2007). Role of Shape Parameters in Speech Recognition: A study on standard colloquial Bengali (SCB), PhD Thesis, Jadavpur University, Kolkata, India.

  • Das Mandal, S., Gupta, B., & Datta, A. (2007). Word boundary detection based on suprasegmental features: A case study on Bangla speech. International Journal of Speech Technology,9(1–2), 17–28.

    Article  Google Scholar 

  • Das Mandal, S., Saha, A., & Datta, A. (2005). Annotated speech corpora development in Indian languages. Vishwa Bharat,6, 49–64.

    Google Scholar 

  • Das Mandal, S., Warsi, A., Basu, T., Hirose, K., & Fujisaki, H. (2010). Analysis and synthesis of F0 contours for Bangla readout speech. In Oriental COCOSDA (pp. 1–6). Kathmandu: IEEE.

  • Fujii, K., Kashioka, H., & Campbell, N. (2003). Target cost of FQ based on polynomial regression in concatenative speech synthesis. In 15th international congress of phonetic sciences (ICPhS-15) (pp. 2577–2580). Barcelona.

  • Fujisaki, H. (1997). Prosody, models, and spontaneous speech. Computing Prosody (pp. 27–42). New York: Springer.

    Google Scholar 

  • Fujisaki, H. (2004). Information, prosody, and modeling -with emphasis on tonal features of speech. In Speech Prosody (pp. 1–10). Nara, Japan: ISCA.

  • Fujisaki, H., & Kawai, H. (1988). Realization of linguistic information in the voice fundamental frequency contour of the spoken Japanese. In International Conference on Acoustic, Speech, and Signal Processing-ICASSP’88 (pp. 663–666). New York: IEEE.

  • Ganguly, N. R., Datta, A. K., & Mukherjee, B. (1998). Acoustic correlates of perceptual stress in Bengali text reading. In International conference on Computational Linguistics, Speech and Document Processing, (pp. B68–B71). ISI Calcutta.

  • Hayes, B., & Lahiri, A. (1991). Bengali intonational phonology. Natural Language & Linguistic Theory,9(1), 47–96.

    Article  Google Scholar 

  • Hirose, K., & Minematsu, N. (2004). Use of prosodic features for speech recognition. In INTERSPEECH (pp. 1445–1448). Jeju Island, Korea: ISCA.

  • Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences,454(1971), 903–995.

    Article  Google Scholar 

  • Iwano, K., & Hirose, K. (1999). Prosodic word boundary detection using statistical modeling of moraic fundamental frequency contours and its use for continuous speech recognition. In International Conference on Acoustics, Speech, and Signal Processing—ICASSP’99 (pp. 133–136). Phoenix: IEEE.

  • Lehiste, I., & Lass, N. (1976). Suprasegmental features of speech. In N. Lass (Ed.), Contemporary issues in experimental phonetics (pp. 225–239). New York: Academic Press.

    Chapter  Google Scholar 

  • Milone, D., & Rubio, A. (2003). Prosodic and accentual information for automatic speech recognition. IEEE Transaction on Speech and Audio Processing,11(4), 321–333.

    Article  Google Scholar 

  • Narusawa, S., Minematsu, N., Hirose, K., & Fujisaki, H. (2002). A method for automatic extraction of model parameters from fundamental frequency contours of speech. In 2002 IEEE International conference on acoustics, speech, and signal processing (Vol. 1, pp. 506–509). Orlando, Florida: IEEE.

    Google Scholar 

  • Rajendran, S., & Yegnanarayana, B. (1996). Word boundary hypothesization for continuous speech in Hindi based on F0 patterns. Speech Communication,18(1), 21–46.

    Article  Google Scholar 

  • Rilling, G., Flandrin, P., & Goncalves, P. (2003). On empirical mode decomposition and its algorithms. In IEEE-EURASIP workshop on nonlinear signal and image processing (Vol. 3, pp. 8–11). NSIP-03, Grado (I).

  • Sagisaka, Y., Campbell, N., & Higuchi, N. (2012). Computing PROSODY: Computational models for processing spontaneous speech. Kyoto: Springer Science and Business Media.

    Google Scholar 

  • Tsiartas, A., Ghosh, P., Georgiou, P., & Narayanan, S. (2009). Robust word boundary detection in spontaneous speech using acoustic and lexical cues. In International Conference on Acoustics, Speech, and Signal Processing—ICASSP’2009 (pp. 4785–4788). Taipei, Taiwan: IEEE.

  • Vergyri, D., Stolcke, A., Gadde, V., Ferrer, L., & Shriberg, E. (2003). Prosodic knowledge sources for automatic speech recognition. In International Conference on Acoustics, Speech, and Signal Processing—ICASSP’2003 (pp. I–I). Honk Kong: IEEE.

Download references

Author information

Authors and Affiliations

  1. Bennett University, Greater Noida, Uttar Pradesh, India

    Tanmay Bhowmik

  2. CET, Indian Institute of Technology Kharagpur, Kharagpur, India

    Shyamal Kumar Das Mandal

Authors
  1. Tanmay Bhowmik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shyamal Kumar Das Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tanmay Bhowmik.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhowmik, T., Das Mandal, S.K. Prosodic word boundary detection from Bengali continuous speech. Lang Resources & Evaluation 54, 747–765 (2020). https://doi.org/10.1007/s10579-019-09478-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10579-019-09478-0

Keywords

Search

Navigation