Speech Rhythm Measures: Acoustic Cues for Speaker Identification

Document Type : Research Paper

Authors

1 Assistant Professor of Linguistics, University of Isfahan, Isfahan, Iran.

2 Associate Professor of Linguistics, University of Isfahan, Isfahan, Iran.

Abstract

Rhythmic characteristics of speech based on consonantal and vocalic intervals as well as syllabic intervals vary between speakers of the same language. Nonetheless, the rhythmicity of a speech signal is not solely dependent on the durational variability of phonetic intervals but it is also associated with the variability of the intensity patterns as well. Acoustic parameter of intensity is largely determined by the articulatory behaviors of the speech organs such as lip movement or mouth aperture. Therefore, it is plausible that speaker idiosyncrasy in movement of speech articulators and anatomical differences in individual’s vocal tracts may influence the energy distribution across a speech signal which subsequently leads to the variability in the values of the intensity measures. Using experimental phonetics tools and from an explicitly speaker-specific perspective, the present research attempts to explore potential speaker-specific acoustic parameters of speech rhythm which are extracted from the intensity contours across Persian speakers. This research aims to discover whether intensity-based measures of speech rhythm are able to discriminate between speakers in Persian. Two types of acoustic rhythmic measures based on the mean syllable intensity (stdevM, varcoM, rPVIm, nPVIm) and peak syllable intensity (stdevP, varcoP, rPVIp, nPVIp))  were selected for this study. Speech data from 12 Persian male speakers were recorded non-contemporaneously in laboratory environment on two different occasions separated by one to two weeks. Speech tokens were acoustically measured with PRAAT version 5.2.34 and statistical analyses were carried out with SPSS version 21 and R version 3.3.3. Results of the study indicated that speech rhythm measures based on intensity fluctuations play an important role in between-speaker rhythmic variability. In addition, discriminatory power of intensity-based measures is not affected by the language-dependent characteristics of Persian. The results also showed that the peak syllable intensity measures carry more speaker-specific information compared to the mean syllable intensity measures

Keywords

References

Abercrombie, D. 1967. Elements of general phonetics. Edinburgh: Edinburgh University Press.
Arvaniti, A. 2012. The usefulness of metrics in the quantification of speech rhythm.  Journal of Phonetics, 40(3), 351–373.
Asadi, H., Nourbakhsh, M., He, L., Pellegrino, E. and Dellwo, V. 2018. Between-speaker rhythmic variability is not dependent on language rhythm, as evidence from Persian reveals. International Journal of Speech, Language and the Law, 25(2), 151-174.     
Asadi, H., He, L., Pellegrino, E. and Dellwo, V. 2017. Between-speaker rhythmic variability in Persian. The 26th annual conference of the International Association for Forensic Phonetics and Acoustics (IAFPA). Split, Croatia.
Boersma, P. and Weenink, D. 2013. Praat: Doing Phonetics by Computer. http://www.praat.org, Accessed 13 July 2013.
Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A. and Ghazanfar, A.A. 2009. The natural statistics of audiovisual speech. PLoS Computational Biology, 5(7), e1000436.
Dellwo, V. 2010. Influences of speech rate on the acoustic correlates of speech rhythm: An experimental phonetic study based on acousticand perceptual evidence. PhD dissertation, Bonn University.
Dellwo, V., Leeman, A. and Kolly, M. 2015. Rhythmic variability between speakers: Articulatory, prosodic, and linguistic factors. Journal of the Acoustical Society of America, 137:1513-1528.
Erickson, D., Kim, J., Kawahara, S., Wilson, I., Menezes, C., Suemitsu,         A. and Moore, J. 2015. Bridging articulation and perception: TheC/D model and contrastive emphasis. In   Proceedings of the 18th International Congress of Phonetic Sciences (ICPhS), 1–5. Glasgow, UK.                     
Fuchs, R. 2016. Speech rhythm in varieties of English. Singapore: Springer.
Garnier, M., Wolfe, J., Henrich, N. and Smith, J. 2008. Interrelationship between vocal effort and vocal tract acoustics: a pilot study. In Proceedings of INTERSPEECH, 2302-2305. Brisbane, Australia.
Grabe, E. and Low, E. L. 2002. “Durational variability in speech and rhythm class hypothesis”. In N. Warner & C. Gussenhoven          (Eds.), Papers in Laboratory Phonology 7, 515-543, Berlin and New York: Mouton de Gruyter.
He, L. and Dellwo, V. 2016. The role of syllable intensity in between-speaker rhythmic variability. International Journal of Speech, Language and the Law. Vol 23, 243-273.
He, L., and Dellwo, V. 2014. Speaker idiosyncratic variability of intensity across syllables. In Proceedings of INTERSPEECH, 233-237. Singapore.
IBM Corp. 2012. IBM SPSS Statistics for Windows (version 21.0). Armonk, NY: International Business Machines Corporation.
Leemann, A., Kolly, M.-J., and Dellwo, V. 2014. Speaker-individuality insuprasegmental temporal features: implications for forensic voice comparison.           Forensic Science International, 238, 59-67.     
Loukina, A., Kochanski, G., Rosner, B., Keane, E. and Shih, C. 2011. Rhythm measures and dimensions of durational variation in speech. Journal of the Acoustical Society of America, 129(5),3258–3270.
Nespor, M., Shukla, M. Mehler, J. 2011. Stress-timed vs. syllable- timed languages. In M. van Oostendorp, C. J. Ewen, E. Hume & K. Rice (eds.), The Blackwell Companion to Phonology, (pp. 1147–1159).
Perrier, P. 2012. Gesture planning integrating knowledge of the motor plant’s dynamics: a literature review from motor control and speech motor control. In S. Fuchs, M. Weirich, D. Pape and P.           Perrier (eds) Speech Planning and Dynamics 191–238.            Frankfurt         am Main: Peter Lang.  
Pike, K. L. 1946. Intonation of American English. Ann Arbor: University of Michigan.
R Core Team 2014. R: A Language and Environment for Statistical Computing(version3.3.3). R Foundation for Statistical Computing. http: // www.Rproject.org, Accessed 20 November              2016. 
Ramus, F., Nespor, M. and Mehler, J. 1999. Correlates of linguistic rhythm in the speech signal. Cognition, 73, 265-292.
Tilsen, S. and Arvaniti, A. 2013. Speech rhythm analysis withdecomposition of the amplitude envelope: Characterizing rhythmic patterns within and across languages. Journal of the        Acoustical Society of America, 134(1), 628–639.
White, L., Mattys, S.L. 2007. Calibrating rhythm: First language and second language studies”, Journal of Phonetics, 35(4), 501–522.
Wiget, L., White, L., Schuppler, B., Grenon, I., Rauch, O., and Mattys, S. L. 2010. How stable are acoustic metrics of contrastive speech   rhythm? Journal of the Acoustical Society of America, 127(3),           1559–156.
Language Research
Volume 12, Issue 2
December 2022
Pages 29-49

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History

  • Receive Date: 22 July 2020
  • Revise Date: 03 October 2020
  • Accept Date: 02 January 2021
  • First Publish Date: 02 January 2021

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