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Exploring the front fricative contrast in Greek: A study of acoustic variability based on cepstral coefficients

Published online by Cambridge University Press:  03 June 2020

Jason Lilley Open the ORCID record for Jason Lilley [Opens in a new window] ,
Laura Spinu  and
Angeliki Athanasopoulou
Jason Lilley
Affiliation:
Center for Pediatric and Auditory Speech Sciences, Nemours Biomedical Research, Wilmington, DE, USA jason.lilley@nemours.org
Laura Spinu
Affiliation:
Department of Communications & Performing Arts, CUNY Kingsborough Community College, Brooklyn, NY, USA lspinu@kbcc.cuny.edu
Angeliki Athanasopoulou
Affiliation:
School of Languages, Linguistics, Literatures and Cultures, University of Calgary, Calgary, AB, Canada angeliki.athanasopou@ucalgary.ca
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Abstract

In the current study, we explore the factors underlying the well-known difficulty in acoustic classification of front nonsibilant fricatives (Maniwa, Jongman & Wade 2009, McMurray & Jongman 2011) by applying a novel classification method to the production of Greek speakers. The Greek fricative inventory [f v θ ð s z ç ʝ x ɣ] includes voiced and voiceless segments from five distinct places of articulation. Our corpus contains all of the Greek fricatives produced by 29 monolingual speakers, but our focus is on the distinction between the front nonsibilant fricatives [f v θ ð]. For comparison, we also discuss the other places of articulation where relevant. We apply a relatively novel classification method based on cepstral coefficients, previously successful in categorizing English obstruent bursts (Bunnell, Polikoff & McNicholas 2004), English vowels (Ferragne & Pellegrino 2010), Romanian fricatives (Spinu & Lilley 2016), and Russian fricatives (Spinu, Kochetov & Lilley 2018). For this study, fricative boundaries were automatically aligned using Hidden Markov Models (HMMs) and then manually checked. Six Bark-frequency cepstral coefficients (c0–c5) were extracted from 20-millisecond Hann windows. HMMs were used to divide the fricatives and adjacent vowels into three regions of internally minimized variance. A multinomial logistic regression analysis then used the mean cepstral coefficients from each region as predictors for classification by consonant identity. Our method yields highly successful classification rates, exceeding the performance of previous methods. We discuss these results in light of the differences of the phonemic distributions of fricatives between English and Greek.

Keywords

fricativesGreekcepstral coefficientsHidden Markov modelsclassification

Information

Type
Research Article
Information
Journal of the International Phonetic Association , Volume 51 , Issue 3 , December 2021 , pp. 393 - 424
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Copyright
© International Phonetic Association 2020

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References

Al-Khairy, Mohamed Ali. 2005. Acoustic characteristics of Arabic fricatives. Ph.D. dissertation, University of Florida.Google Scholar
Barber, Charles, Beal, Joan C. & Shaw, Philip A.. 2009. The English language: A historical introduction, 2nd edn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Blacklock, Oliver S. 2004. Characteristics of variation in production of normal and disordered fricatives, using reduced-variance spectral methods. Ph.D. dissertation, University of Southampton.Google Scholar
Blackman, R. B. & Tukey, J. W.. 1958. The measurement of power spectra from the point of view of communications engineering – Part I. Bell System Technical Journal 37(1), 185–282.CrossRefGoogle Scholar
Bladon, R. A. W. & Lindblom, Björn. 1981. Modeling the judgment of vowel quality differences. The Journal of the Acoustical Society of America 69(5), 1414–1422.CrossRefGoogle ScholarPubMed
Bogdanov, Dmitry, Wack, Nicholas, Gutiérrez, Emilia Gómez, Gulati, Sankalp, Boyer, Perfecto Herrera, Mayor, Oscar, Trepat, Gerard Roma, Salamon, Justin, González, José Ricardo Zapata & Serra, Xavier. 2013. Essentia: An audio analysis library for music information retrieval. In Britto, Alceu S. , Jr., Gouyon, Fabien & Dixon, Simon (eds.), 14th Conference of the International Society for Music Information Retrieval Conference (ISMIR ’13), Curitiba, Brazil, 493–498. http://essentia.upf.edu/ (accessed 3 December 2019).Google Scholar
Bunnell, H. Timothy, Polikoff, James & McNicholas, Jane. 2004. Spectral moment vs. Bark cepstral analysis of children’s word-initial voiceless stops. 5th International Conference on Spoken Language Processing (Interspeech 2004), Jeju Island, Korea, 1313–1316.Google Scholar
Cooley, James W. & Tukey, John W.. 1965. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation 19, 297–301.CrossRefGoogle Scholar
DiCanio, Christian, Nam, Hosung, Whalen, Douglas H., Bunnell, H. Timothy, Amith, Jonathan D. & García, Rey Castillo. 2013. Using automatic alignment to analyze endangered language data: Testing the viability of untrained alignment. The Journal of the Acoustical Society of America 134(3), 2235–2246.CrossRefGoogle ScholarPubMed
Eyben, Florian, Wöllmer, Martin & Schuller, Björn. 2010. openSMILE – The Munich Versatile and Fast Open-Source Audio Feature Extractor. The 18th ACM International Conference on Multimedia (ACM-MM 2010), Florence, Italy, 1459–1462. https://www.audeering.com/opensmile/ (accessed 3 December 2019).CrossRefGoogle Scholar
Ferragne, Emmanuel & Pellegrino, François. 2010. Vowel systems and accent similarity in the British Isles: Exploiting multidimensional acoustic distances in phonetics. Journal of Phonetics 38(4), 526–539.CrossRefGoogle Scholar
Forrest, Karen, Weismer, Gary, Milenkovic, Paul & Dougall, Ronald N.. 1988. Statistical analysis of word-initial voiceless obstruents: Preliminary data. The Journal of the Acoustical Society of America 84, 115–124.CrossRefGoogle Scholar
Garofolo, John S., Lamel, Lori F., Fisher, William M., Fiscus, Jonathan G., Pallett, David S., Dahlgren, Nancy L. & Zue, Victor. 1993. The DARPA TIMIT acoustic-phonetic continuous speech corpus. Linguistic Data Consortium.CrossRefGoogle Scholar
Granqvist, Kimmo. 2002. Similarity and frequency in Modern Greek phonology. Stockholm: Almqvist & Wiksell International.Google Scholar
Harris, Zellig S. 1954. Distributional structure. Word 10(2–3), 146–162.CrossRefGoogle Scholar
Hayes, Bruce & Steriade, Donca. 2004. Introduction: The phonetic bases of phonological markedness. In Hayes, Bruce, Kirchner, Robert & Steriade, Donca (eds.), Phonetically based phonology, 1–33. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Jekiel, Mateusz. 2012. The evolution of English dental fricatives: Variation and change. Ph.D. dissertation, Adam Mickiewicz University.Google Scholar
Jesus, Luis M. T. & Jackson, Philip J. B.. 2008. Frication and voicing classification. In Teixeira, António, de Lima, Vera Lúcia Strube, de Oliveira, Luís Caldas & Quaresma, Paulo (eds.), Computational processing of the Portuguese language (PROPOR 2008) (Lecture Notes in Computer Science, vol. 5190), 11–20. Berlin & Heidelberg: Springer.CrossRefGoogle Scholar
Jesus, Luis M. T. & Shadle, Christine H.. 2002. A parametric study of the spectral characteristics of European Portuguese fricatives. Journal of Phonetics 30(3), 437–464.CrossRefGoogle Scholar
Jongman, Allard, Wayland, Ratree & Wong, Serena. 2000. Acoustic characteristics of English fricatives. The Journal of the Acoustical Society of America 108, 1252–1263.CrossRefGoogle ScholarPubMed
Joseph, Brian D. & Philippaki-Warburton, Irene. 1987. Modern Greek. London: Croom Helm.Google Scholar
Kalimeris, Constandinos & Bakamidis, Stelios. 2007. Minimal pairs and functional loads of sound contrasts obtained from a list of Modern Greek words. 8th International Conference of the International Speech Communication Association (Interspeech 2007), Antwerp, 998–1001.Google Scholar
Kingston, John & Diehl, Randy L.. 1994. Phonetic knowledge. Language 70(3), 419–454.CrossRefGoogle Scholar
Kochetov, Alexei. 1999. A hierarchy of phonetic constraints on palatality in Russian. University of Pennsylvania Working Papers in Linguistics 6(1), article 18. https://repository.upenn.edu/pwpl/vol6/iss1/18/ (accessed 20 October 2019).Google Scholar
Kochetov, Alexei. 2002. Production, perception, and emergent phonotactic patterns: A case of contrastive palatalization. Ph.D. dissertation, University of Toronto.Google Scholar
Kong, Ying-Yee, Mullangi, Ala & Kokkinakis, Kostas. 2014. Classification of fricative consonants for speech enhancement in hearing devices. PLoS ONE 9(4), e95001. doi:10.1371/journal.pone.0095001.CrossRefGoogle ScholarPubMed
Lass, Roger. 1994. Old English: A historical linguistic companion. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Liljencrants, Johan & Lindblom, Björn. 1972. Numerical simulation of vowel quality systems: The role of perceptual contrast. Language 48(4), 839–862.CrossRefGoogle Scholar
Maniwa, Kazumi, Jongman, Allard & Wade, Travis. 2009. Acoustic characteristics of clearly spoken English fricatives. The Journal of the Acoustical Society of America 125(6), 3962–3973.CrossRefGoogle ScholarPubMed
McMurray, Bob & Jongman, Allard. 2011. What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations. Psychological Review 118(2), 219–246.CrossRefGoogle ScholarPubMed
Mermelstein, Paul. 1976. Distance measures for speech recognition – psychological and instrumental. In Chen, C. H. (ed.), Pattern recognition and artificial intelligence, 374–388. New York: Academic Press.Google Scholar
Narayanan, Shrikanth, Alwan, Abeer A. & Haker, Katherine. 1995. An articulatory study of fricative consonants using magnetic resonance imaging. The Journal of the Acoustical Society of America 98(3), 1325–1347.CrossRefGoogle Scholar
Nirgianaki, Elina. 2014. Acoustic characteristics of Greek fricatives. The Journal of the Acoustical Society of America 135(5), 2964–2976.CrossRefGoogle ScholarPubMed
Nirgianaki, Elina, Chaida, Anthi & Fourakis, Marios. 2010. Acoustic structure of fricative consonants in Greek. In Botinis, Antonis, Fourakis, Marios & Gawronska, Barbara (eds.), 3rd ISCA Workshop on Experimental Linguistics (ExLing-2010), Athens, 125–128.Google Scholar
Nissen, Shawn L. & Fox, Robert Allen. 2005. Acoustic and spectral characteristics of young children’s fricative productions: A developmental perspective. The Journal of the Acoustical Society of America 118(4), 2570–2578.CrossRefGoogle ScholarPubMed
Reidy, Patrick F. 2015. A comparison of spectral estimation methods for the analysis of sibilant fricatives. The Journal of the Acoustical Society of America 137(4), EL248–EL254.CrossRefGoogle ScholarPubMed
Shadle, Christine H. 2006. Phonetics, acoustic. In Brown, Keith (ed.), Encyclopedia of language and linguistics, 2nd edn., vol. 9, 442–460. Oxford: Elsevier.CrossRefGoogle Scholar
Smith, Bridget. 2010. The incomplete phonologization of the non-sibilant dental fricatives in American English. Ms., The Ohio State University.Google Scholar
Smith, Bridget. 2013. An acoustic analysis of voicing in American English dental fricatives. Ohio State University Working Papers in Linguistics 60, 117–128. http://kb.osu.edu/handle/1811/80994/ (accessed 1 March 2017).Google Scholar
Spinu, Laura, Kochetov, Alexei & Lilley, Jason. 2018. Acoustic classification of Russian plain and palatalized sibilant fricatives: Spectral vs. cepstral measures. Speech Communication 100, 41–45.CrossRefGoogle Scholar
Spinu, Laura & Lilley, Jason. 2016. A comparison of cepstral coefficients and spectral moments in the classification of Romanian fricatives. Journal of Phonetics 57, 40–58.CrossRefGoogle Scholar
Spinu, Laura, Vogel, Irene & Bunnell, H. Timothy. 2012. Palatalization in Romanian: Acoustic properties and perception. Journal of Phonetics 40(1), 54–66.CrossRefGoogle Scholar
Steriade, D. 1999. Phonetics in phonology: The case of laryngeal neutralization. In Gordon, Matthew K. (ed.), UCLA Working Papers in Linguistics 2: Papers in Phonology 3, 25–146. Los Angeles: University of California.Google Scholar
Stevens, Kenneth N[oble] & Keyser, Samuel Jay. 1989. Primary features and their enhancement in consonants. Language 65(1), 81–106.CrossRefGoogle Scholar
Stevens, Kenneth Noble & Keyser, Samuel Jay. 2010. Quantal theory, enhancement and overlap. Journal of Phonetics 38(1), 10–19.CrossRefGoogle Scholar
Stevens, Kenneth N[oble], Keyser, Samuel Jay & Kawasaki, Haruko. 1986. Toward a phonetic and phonological theory of redundant features. In Perkell, Joseph S. & Klatt, Dennis H. (eds.), Invariance and variability in speech processes, 426–449. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Thomson, David J. 1982. Spectrum estimation and harmonic analysis. Proceedings of the IEEE 70(9), 1055–1096.CrossRefGoogle Scholar
Tomiak, Gail R. 1990. An acoustic and perceptual analysis of the spectral moments invariant with voiceless fricative obstruents. Ph.D. dissertation, SUNY Buffalo.CrossRefGoogle Scholar
Viterbi, Andrew J. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269.CrossRefGoogle Scholar
Yarrington, Debra, Gray, John, Pennington, Chris, Bunnell, H. Timothy, Cornaglia, Allegra, Lilley, Jason, Nagao, Kyoko & Polikoff, James. 2008. ModelTalker Voice Recorder: An interface system for recording a corpus of speech for synthesis. In Lin, Jimmy (ed.), Proceedings of the ACL-08: HLT Demo Session (Companion Volume), Columbus, OH, Association for Computational Linguistics, 28–31.Google Scholar
Young, Steve, Evermann, Gunnar, Gales, Mark, Hain, Thomas, Kershaw, Dan, Liu, Xunying, Moore, Gareth, Odell, Julian, Ollason, Dave, Povey, Dan, Valtchev, Valtcho & Woodland, Phil. 2009. The HTK Book (for HTK Version 3.4.1). http://htk.eng.cam.ac.uk/ (accessed 3 December 2019).Google Scholar