Behavior Research Methods

, Volume 41, Issue 2, pp 385–390 | Cite as

Praat script to detect syllable nuclei and measure speech rate automatically

Article

Abstract

In this article, we describe a method for automatically detecting syllable nuclei in order to measure speech rate without the need for a transcription. A script written in the software program Praat (Boersma & Weenink, 2007) detects syllables in running speech. Peaks in intensity (dB) that are preceded and followed by dips in intensity are considered to be potential syllable nuclei. The script subsequently discards peaks that are not voiced. Testing the resulting syllable counts of this script on two corpora of spoken Dutch, we obtained high correlations between speech rate calculated from human syllable counts and speech rate calculated from automatically determined syllable counts. We conclude that a syllable count measured in this automatic fashion suffices to reliably assess and compare speech rates between participants and tasks.

References

  1. Boersma, P., & Weenink, D. (2007). Praat (Version 4.5.25) [Software]. Latest version available for download from www.praat.org.Google Scholar
  2. Cannizzaro, M., Harel, B., Reilly, N., Chappell, P., & Snyder, P. J. (2004). Voice acoustical measurement of the severity of major depression. Brain & Cognition, 56, 30–35. doi:10.1016/j.bandc.2004.05.003CrossRefGoogle Scholar
  3. Covington, M. A., He, C., Brown, C., Naçi, L., McClain, J. T., Fjordbak, B. S., et al. (2005). Schizophrenia and the structure of language: The linguist’s view. Schizophrenia Research, 77, 85–98. doi:10.1016/j.schres.2005.01.016CrossRefPubMedGoogle Scholar
  4. Cucchiarini, C., Strik, H., & Boves, L. (2002). Quantitative assessment of second language learners’ fluency: Comparisons between read and spontaneous speech. Journal of the Acoustical Society of America, 111, 2862–2873. doi:10.1121/1.1471894CrossRefPubMedGoogle Scholar
  5. Hulstijn, J. H. (2007). The effect of task complexity on fluency and functional adequacy of speaking performance. In S. Van Daele, A. Housen, M. Pierrard, F. Kuiken, & I. Vedder (Eds.), Complexity, accuracy and fluency in second language use, learning and teaching (pp. 53–63). Brussels: Koninklijke Vlaamse Academie van België voor Wetenschappen en Kunsten.Google Scholar
  6. de Jong, N. H., & Wempe, T. (2008). Praat script speech rate. Retrieved October 14, 2008, from sites.google.com/site/speechrate/.Google Scholar
  7. Educational Testing Service (2004). iBT/Next Generation TOEFL Test: Independent Speaking Rubrics. Retrieved December 10, 2007, from www.ets.org/Media/Tests/TOEFL/pdf/Speaking_Rubrics.pdf.Google Scholar
  8. Ernestus, M. T. C. (2000). Voice assimilation and segment reduction in casual Dutch: A corpus-based study of the phonology—phonetics interface. Ph.D. dissertation, Vrije Universiteit, Amsterdam (LOT Series 36).Google Scholar
  9. Feyereisen, P., Pillon, A., & de Partz, M.-P. (1991). On the measures of fluency in the assessment of spontaneous speech production by aphasic subjects. Aphasiology, 5, 1–21. doi:10.1080/02687039108248516CrossRefGoogle Scholar
  10. Hunt, A. (1993). Recurrent neural networks for syllabification. Speech Communication, 13, 323–332. doi:10.1016/0167-6393(93)90031-FCrossRefGoogle Scholar
  11. Kormos, J., & Dénes, M. (2004). Exploring measures and perceptions of fluency in the speech of second language learners. System, 32, 145–164. doi:10.1016/j.system.2004.01.001CrossRefGoogle Scholar
  12. Mermelstein, P. (1975). Automatic segmentation of speech into syllabic units. Journal of the Acoustical Society of America, 58, 880–883. doi:10.1121/1.380738CrossRefPubMedGoogle Scholar
  13. O’Brien, I., Segalowitz, N., Freed, B., & Collentine, J. (2007). Phonological memory predicts second language oral fluency gains in adults. Studies in Second Language Acquisition, 29, 557–582. doi:10.1017/S027226310707043XGoogle Scholar
  14. Pellegrino, F., & Andre-Obrecht, R. (2000). Automatic language identification: An alternative approach to phonetic modelling. Signal Processing, 80, 1231–1244. doi:10.1016/S0165-1684(00)00032-3CrossRefGoogle Scholar
  15. Pellegrino, F., Farinas, J., & Rouas, J.-L. (2004). Automatic estimation of speaking rate in multilingual spontaneous speech. Proceedings of Speech Prosody 2004, Nara, Japan (pp. 517–520).Google Scholar
  16. Pfau, T., Faltlhauser, R., & Ruske, G. (2000). A combination of speaker normalization and speech rate normalization for automatic speech recognition. Proceedings of ICSLP 2000, Peking, China, 4, 362–365.Google Scholar
  17. Pfau, T., & Ruske, G. (1998). Estimating the speaking rate by vowel detection. Acoustics, Speech, and Signal Processing (ICASSP 2005 Proceedings), 2, 945–948. doi:10.1109/ICASSP.1998.675422Google Scholar
  18. Pfitzinger, H. R. (1999). Local speech rate perception in German speech. Proceedings of the XIVth International Congress of Phonetic Sciences, 2, 893–896.Google Scholar
  19. Redmond, S. (2004). Conversational profiles of children with ADHD, SLI and typical development. Clinical Linguistics & Phonetics, 18, 107–125. doi:10.1080/02699200310001611612CrossRefGoogle Scholar
  20. Riggenbach, H. (1991). Toward an understanding of fluency: A microanalysis of nonnative speaker conversations. Discourse Processes, 14, 423–441.CrossRefGoogle Scholar
  21. Shenker, R. C. (2006). Connecting stuttering management and measurement: I. Core speech measures of clinical process and outcome. International Journal of Language & Communication Disorders, 41, 355–364. doi:10.1080/13682820600623861CrossRefGoogle Scholar
  22. Tavakoli, P., & Skehan, P. (2005). Strategic planning, task structure, and performance testing. In R. Ellis (Ed.), Planning and task performance in a second language (pp. 239–276). Amsterdam: John Benjamins.Google Scholar
  23. Towell, R., Hawkins, R., & Bazergui, N. (1996). The development of fluency in advanced learners of French. Applied Linguistics, 17, 84–119. doi:10.1093/applin/17.1.84CrossRefGoogle Scholar
  24. van Son, R. J. J. H., Binnenpoorte, D., van den Heuvel, H., & Pols, L. C. W. (2001). The IFA corpus: A phonemically segmented Dutch “open source” speech database. EUROSPEECH 2001, 2051–2054.Google Scholar
  25. Verhasselt, J. P., & Martens, J. P. (1996). A fast and reliable rate of speech detector. Spoken Language (ICSLP 96 Proceedings), 4, 2258–2261. doi:10.1109/ICSLP.1996.607256CrossRefGoogle Scholar
  26. Wang, D., & Narayanan, S. (2007). Robust speech rate estimation for spontaneous speech. IEEE Transactions on Speech, Audio and Language Processing, 15, 2190–2201. doi:10.1109/TASL.2007.905178CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2009

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Dutch Language and CultureUtrecht UniversityUtrechtThe Netherlands

Personalised recommendations