Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Linguistics Vanguard

A Multimodal Journal for the Language Sciences

Editor-in-Chief: Bergs, Alexander / Cohn, Abigail C. / Good, Jeff

Online
ISSN
2199-174X
See all formats and pricing
More options …

Practice makes perfect: the consequences of lexical proficiency for articulation

Fabian Tomaschek / Benjamin V. Tucker / Matteo Fasiolo
  • School of Mathematics, University of Bristol, Bristol, United Kingdom of Great Britain and Northern Ireland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ R. Harald Baayen
Published Online: 2018-09-06 | DOI: https://doi.org/10.1515/lingvan-2017-0018

Abstract

Many studies report shorter acoustic durations, more coarticulation and reduced articulatory targets for frequent words. This study investigates a factor ignored in discussions on the relation between frequency and phonetic detail, namely, that motor skills improve with experience. Since frequency is a measure of experience, it follows that frequent words should show increased articulatory proficiency. We used EMA to test this prediction on German inflected verbs with [a] as stem vowels. Modeling median vertical tongue positions with quantile regression, we observed significant modulation by frequency of the U-shaped trajectory characterizing the articulation of the [a:]. These modulations reflect two constraints, one favoring smooth trajectories through anticipatory coarticulation, and one favoring clear articulation by realizing lower minima. The predominant pattern across sensors, exponents, and speech rate suggests that the constraint of clarity dominates for lower-frequency words. For medium-frequency words, the smoothness constraint leads to a raising of the trajectory. For the higher-frequency words, both constraints are met simultaneously, resulting in low minima and stronger coarticulation. These consequences of motor practice for articulation challenge both the common view that a higher-frequency of use comes with more articulatory reduction, and cognitive models of speech production positing that articulation is post-lexical.

Keywords: coarticulation; frequency of use; predictability; quantile regression; generalized additive models

References

  • Arnold, D., F. Tomaschek, K. Sering, M. Ramscar & R. H. Baayen. 2017. Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLoS One. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174623.PubMed

  • Aylett, M. & A. Turk. 2004. The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and Speech 47(1). 31–56.PubMedCrossrefGoogle Scholar

  • Aylett, M. & A. Turk. 2006. Language redundancy predicts syllabic duration and the spectral characteristics of vocalic syllable nuclei. Journal of the Acoustical Society of America 119(5). 3048–3058.CrossrefGoogle Scholar

  • Baayen, R. H., S. Vasishth, D. Bates & R. Kliegl. 2017a. The cave of shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language 94. 206–234.CrossrefGoogle Scholar

  • Baayen, R. H., F. Tomaschek, S. Gahl & M. Ramscar. 2017b. The Ecclesiastes principle in language change. In M. Hundt, S. Mollin & S. Pfenninger (eds.), The changing English language: Psycholinguistic perspectives, 21–48. Cambridge, UK: Cambridge University Press.Google Scholar

  • Barbier, G., P. Perrier, L. Menard, Y. Payan, M. Tiede & J. Perkell. 2015. Speech planning in 4-year-old children versus adults: Acoustic and articulatory analyses. 16th Annual Conference of the International Speech Communication Association (Interspeech 2015). https://hal.archives-ouvertes.fr/hal-01200984 (accessed 1 February 2018).

  • Bell, A., J. M. Brenier, M. Gregory, C. Girand & D. Jurafsky. 2009. Predictability effects on durations of content and function words in conversational English. Journal of Memory and Language 60(1). 92–111.CrossrefGoogle Scholar

  • Bertucco, M. & P. Cesari. 2010. Does movement planning follow Fitts’ law? Scaling anticipatory postural adjustments with movement speed and accuracy. Neuroscience 171(1). 205–213.CrossrefPubMedGoogle Scholar

  • Blevins, J. P., P. Milin & M. Ramscar. 2015. The Zipfian paradigm cell filling problem. In F. Kiefer, J. P. Blevins & H. Bartos (eds.), Morphological paradigms and functions, 141–158. Leiden: Brill.Google Scholar

  • Boersma, P. & P. Weenink. 2015. Praat: Doing phonetics by computer [computer program], version 5.3.41, retrieved from http://www.praat.org/.

  • Browman, C. & L. Goldstein. 1986. Towards an articulatory phonology. Phonology 3. 219–252.CrossrefGoogle Scholar

  • Browman, C. & L. Goldstein. 1989. Articulatory gestures as phonological units. Phonology 6. 201–251.CrossrefGoogle Scholar

  • Clopper, C. G., R. Turnbull & R. S. Burdin. 2018. Assessing predictability effets in connected read speech. Linguistics Vanguard 4(S2).Google Scholar

  • Cohen Priva, U. 2015. Informativity affects consonant duration and deletion rates. Laboratory Phonology 6(2). 243–278.Google Scholar

  • Cohen Priva, U. & F. Jaeger. 2018. The interdependence of frequency, predictability, and informativity. Linguistics Vanguard 4(S2).Google Scholar

  • Daland, R. & K. Zuraw. 2018. Loci and locality of informational effects on phonetic implementation. Linguistics Vanguard 4(S2).Google Scholar

  • Dell, G. S. 1986. A spreading-activation theory of retrieval in sentence production. Psychological Review 93(3). 283–321.CrossrefPubMedGoogle Scholar

  • Ernestus, M., R. H. Baayen & R. Schreuder. 2002. The recognition of reduced word forms. Brain and Language 81(1–3). 162–173.CrossrefPubMedGoogle Scholar

  • Faaß, G. & K. Eckart. 2013. Sdewac – a corpus of parsable sentences from the web. In I. Gurevych, C. Biemann & T. Zesch (eds.), Language processing and knowledge in the web (Lecture Notes in Computer Science), 61–68. Berlin/Heidelberg: Springer.Google Scholar

  • Fasiolo, M., Y. Goude, R. Nedellec & S. N. Wood. 2017. Fast calibrated additive quantile regression. Manuscript, University of Bristol. https://github.com/mfasiolo/qgam.

  • Fitts, Paul M. 1954. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47(6). 381.CrossrefPubMedGoogle Scholar

  • Foulkes, P., G. Docherty, S. Shattuck-Hufnagel & V. Hughes. 2018. Consideration of methodological robustness, indexical and prosodic factors, and replication in the laboratory. Linguistics Vanguard 4(S2).Google Scholar

  • Gahl, S. 2008. “Thyme” and “time” are not homophones. Word durations in spontaneous speech. Language 84(3). 474–496.CrossrefGoogle Scholar

  • Gahl, S. & R. H. Baayen. under revision. Twenty-eight years of vowels.Google Scholar

  • Gahl, S., Y. Yao & K. Johnson. 2012. Why reduce? Phonological neighborhood density and phonetic reduction in spontaneous speech. Journal of Memory and Language 66. 789–806.CrossrefGoogle Scholar

  • Georgopoulos, A., J. Kalaska & J. Massey. 1981. Spatial trajectories and reaction times of aimed movements: Effects of practice, uncerntainty, and change in target location. Journal of Neurophysiology 46(4). 725–743.PubMedCrossrefGoogle Scholar

  • Goffman, L., A. Smith, L. Heisler & M. Ho. 2008. The breadth of coarticulatory units in children and adults. Journal of Speech, Language, and Hearing Research 51(6). 1424–1437.CrossrefGoogle Scholar

  • Goldstein, L., H. Nam, E. Saltzman & I. Chitoran. 2009. Coupled oscillator planning model of speech timing and syllable structure. In G. Fant, H. Fujisaki & J. Shen (eds.), Frontiers in phonetics and speech science, 239–250. Beijing: The Commercial Press.Google Scholar

  • Hall, K. C., E. Hume, F. Jaeger & A. Wedel. 2018. The role of predictability in shaping phonological patterns. Linguistics Vanguard 4(S2).Google Scholar

  • Hastie, T. J. & R. J. Tibshirani. 1990. Generalized additive models. London: Chapman & Hall.Google Scholar

  • Hawkins, S. 2003. Roles and representations of systematic fine phonetic detail in speech understanding. Journal of Phonetics 31. 373–405.CrossrefGoogle Scholar

  • Hickok, G. 2014. The architecture of speech production and the role of the phoneme in speech processing. Language, Cognition and Neuroscience 29(1). 2–20.CrossrefGoogle Scholar

  • Johnson, K. 2004. Massive reduction in conversational American English. In K. Yoneyama & K. Maekawa (eds.), Spontaneous speech: Data and analysis. Proceedings of the 1st session of the 10th international symposium, 29–54. Tokyo, Japan: The National International Institute for Japanese Language.Google Scholar

  • Junqua, J. C. 1993. The lombard reflex and its role on human listeners and automatic speech recognizers. The Journal of the Acoustical Society of America 93(1). 510–524.PubMedCrossrefGoogle Scholar

  • Katz, W. F. & S. Bharadway. 2001. Coarticulation in fricative-vowel syllables produced by children and adults: A preliminary report. Clinical Linguistics and Phonetics 15(1). 139–143.CrossrefGoogle Scholar

  • Kemps, R. J., M. Ernestus, R. Schreuder & R. H. Baayen. 2005a. Prosodic cues for morphological complexity: The case of Dutch plural nouns. Memory & Cognition 33(3). 430–446.CrossrefGoogle Scholar

  • Kemps, R. J., Lee H. Wurm, M. Ernestus, R. Schreuder & R. H. Baayen. 2005b. Prosodic cues for morphological complexity in Dutch and English. Language and Cognitive Processes 20(1/2). 43–73.CrossrefGoogle Scholar

  • Keuleers, E., M. Stevens, P. Mandera & M. Brysbaert. 2015. Word knowledge in the crowd: Measuring vocabulary size and word prevalence in a massive online experiment. The Quarterly Journal of Experimental Psychology 8. 1665–1692.Google Scholar

  • Koenker, R. 2005. Quantile regression. Cambridge: Cambridge University Press.Google Scholar

  • Langolf, G. D., D. B. Chaffin & J. A. Foulke. 1976. An investigation of Fitts’ law using a wide range of movement amplitudes. Journal of Motor Behavior 8(2). 113–128.CrossrefPubMedGoogle Scholar

  • Lebedev, S., W. H. Tsui & P. Van Gelder. 2001. Drawing movements as an outcome of the principle of least action. Journal of Mathematical Psychology 45. 43–52.PubMedCrossrefGoogle Scholar

  • Levelt, W. J., A. Roelofs & A. S. Meyer. 1999. A theory of lexical access in speech production. The Behavioral and Brain Sciences 22(1). 1–75.PubMedGoogle Scholar

  • Liberman, A. M. & I. G. Mattingly. 1985. The motor theory of speech perception revised. Cognition 21. 1–36.PubMedCrossrefGoogle Scholar

  • Lindblom, B. 1990. Explaining phonetic variation: A sketch of the H&H theory. English. In W. J. Hardcastle & A. Marchal (eds.), Speech production and speech modelling, vol. 55, 403–439. Dordrecht: Kluwer.Google Scholar

  • Magen, H. S. 1997. The extent of vowel-to-vowel coarticulation in English. Journal of Phonetics 25. 187–205.CrossrefGoogle Scholar

  • Meunier, C. & R. Espesser. 2011. Vowel reduction in conversational speech in French: The role of lexical factors. Journal of Phonetics 39(3). 271–278.CrossrefGoogle Scholar

  • Moon, S.-J. & B. Lindblom. 1989. Formant undershoot in clear and citation- form speech: A second progress report. Stockholm: Royal Institute of Technology, Department of Speech Communication.Google Scholar

  • Noiray, A., L. Menard & K. Iskarous. 2013. The development of motor synergiers in children: Ultrasound and acoustic measurements. Journal of the Acoustical Society of America 133(1). 444–452.CrossrefGoogle Scholar

  • Öhman, S. E. G. 1966. Coarticulation in vcv utterances: Spectrographic measurements. Journal of the Acoustical Society of America 39(151). 151–168.CrossrefGoogle Scholar

  • Platz, T., R. G. Brown & C. D. Marsden. 1998. Training improves the speed of aimed movements in parkinson’s disease. Brain 121. 505–513.CrossrefPubMedGoogle Scholar

  • Pouplier, M., S. Marin, P. Hoole & A. Kochetov. 2017. Speech rate effects in Russian onset clusters are modulated by frequency, but not auditory cue robustness. Journal of Phonetics 64. 108–126.CrossrefGoogle Scholar

  • R Core Team. 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org.

  • Raeder, C., J. Fernandez-Fernandez & A. Ferrauti. 2015. Effects of six weeks of medicine ball training on throwing velocity, throwing precision, and isokinetic strength of shoulder rotators in female handball players. Journal of Strength and Conditioning Research 29(7). 1904–1014.CrossrefPubMedGoogle Scholar

  • Ramscar, M., P. Hendrix, C. Shaoul, P. Milin & R. H. Baayen. 2014. The myth of cognitive decline: Non-linear dynamics of lifelong learning. Topics in Cognitive Science 6(1). 5–42.CrossrefPubMedGoogle Scholar

  • Ramscar, M., C. C. Sun, P. Hendrix & R. H. Baayen. 2017. The mismeasurement of mind: Life-span changes in paired-associate-learning scores reflect the “cost” of learning, not cognitive decline. Psychological Science 28(8). 1171–1179. https://doi.org/10.1177/0956797617706393.PubMedCrossref

  • Rapp, S. 1995. Automatic phonemic transcription and linguistic annotation from known text with Hidden Markov Models – an aligner for German. Proceedings of ELSNET goes east and IMACS Workshop “Integration of Language and Speech.”Google Scholar

  • Schmidtke, D., K. Matsuki & V. Kuperman. 2017. Surviving blind decomposition: A distributional analysis of the time course of complex word recognition. Journal of Experimental Psychology: Learning, Memory and Cognition 43(11). 1793–1820.Google Scholar

  • Schulz, E., Y. M. Oh, Z. Malisz, B. Andreeva & B. Mobius. 2016. Impact of prosodic structure and information density on vowel space size. Proceedings of Speech Prosody 2016 (Boston). 350–354.Google Scholar

  • Shaoul, C. & F. Tomaschek. 2013. A phonological database based on celex and n-gram frequencies from the sdewac corpus. https://fabiantomaschek.files.wordpress.com/2016/07/tomaschek_corpus_readme.pdf (accessed 1 February 2018).

  • Sosnik, R., B. Hauptmann, A. Karni & T. Flash. 2004. When practice leads to co-articulation: The evolution of geometrically defined movement primitives. Experimental Brain Research 156. 422–438.CrossrefPubMedGoogle Scholar

  • Sussman, H. M., C. Duder, E. Dalston & A. Cacciatore. 1999. An acoustic analysis of the development of cv coarticulation – a case study. Journal of Speech, Language, and Hearing Research 42(5). 1080–1096. https://doi.org/10.1044/jslhr.4205.1080. +http://dx.doi.org/10.1044/jslhr.4205.1080.Crossref

  • Tiede, M., C. Mooshammer, L. Goldstein, S. Shattuck-Hufnagel & J. Perkell. 2011. Motor learning of articulator trajectories in the production of novel utterances. Proceedings of the ICPHS XVII. 1986–1989.Google Scholar

  • Tomaschek, F., B. V. Tucker, M. Wieling & R. H. Baayen. 2014. Vowel articulation affected by word frequency. Proceedings of the 10th ISSP, Cologne. 425–428.Google Scholar

  • Tomaschek, F., D. Arnold, Franziska Broker & R. H. R. Baayen. 2018. Lexical frequency co-determines the speed-curvature relation in articulation. Journal of Phonetics. 68. 103–116.CrossrefGoogle Scholar

  • Tomaschek, F., I. Plag, M. Ernestus & R. H. Baayen. under revision(a). How morphological structure affects phonetic encoding: Modeling the duration of morphemic and nonmorphemic s using naive discriminative learning.Google Scholar

  • Tomaschek, F., D. Arnold, J. van Rij, B. V. Tucker & K. Sering. under revision(b). Proficiency effects on the movement precision during the execution of articulatory gestures.Google Scholar

  • Turnbull, R. 2018. Patterns of probabilistic segment deletion/reduction in English and Japanese. Linguistics Vanguard 4(S2).Google Scholar

  • van Bergem, D. R. 1995. Perceptual and acoustic aspects of lexical vowel reduction, a sound change in progress. Speech Communication 16(4). 329–358.CrossrefGoogle Scholar

  • van Rij, J., M. Wieling, R. H. Baayen & H. van Rijn. 2015. itsadug: Interpreting Time Series, Autocorrelated Data Using GAMMs. R package version 0.8.Google Scholar

  • Wieling, M., F. Tomaschek, D. Arnold, M. Tiede, F. Broker, S. Thiele, S. N. Wood & R. H. Baayen. 2016. Investigating dialectal differences using articulography. Journal of Phonetics 59. 122–143.CrossrefGoogle Scholar

  • Wood, S. N. 2006. Generalized additive models: An introduction with r. Boca Raton, Florida, USA: Chapman & Hall/CRC.Google Scholar

  • Wood, S. N. 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73. 3–36.CrossrefGoogle Scholar

  • Wood, S. N. 2013a. A simple test for random effects in regression models. Biometrika 100. 1005–1010.CrossrefGoogle Scholar

  • Wood, S. N. 2013b. On p-values for smooth components of an extended generalized additive model. Biometrika 100. 221–228.CrossrefGoogle Scholar

  • Zharkova, N., N. Hewlett & W. J. Hardcastle. 2011. Coarticulation as an indicator of speech motor control development in children: An ultrasound study. Motor Control 15(1). 118–140.PubMedCrossrefGoogle Scholar

  • Zharkova, N., N. Hewlett & W. J. Hardcastle. 2012. An ultrasound study of lingual coarticulation in/sv/syllables produced by adults and typically developing children. Journal of the International Phonetic Association 42(2). 193–208.CrossrefGoogle Scholar

  • Zipf, G. K. 1949. Human behavior and the principle of least effort. Cambridge: Addison-Wesley Press.Google Scholar

About the article

Received: 2017-04-27

Accepted: 2018-06-26

Published Online: 2018-09-06


Citation Information: Linguistics Vanguard, Volume 4, Issue s2, 20170018, ISSN (Online) 2199-174X, DOI: https://doi.org/10.1515/lingvan-2017-0018.

Export Citation

©2018 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Fabian Tomaschek and Adrian Leemann
The Journal of the Acoustical Society of America, 2018, Volume 144, Number 5, Page EL410
[2]
Kathleen Currie Hall, Elizabeth Hume, T. Florian Jaeger, and Andrew Wedel
Linguistics Vanguard, 2018, Volume 4, Number s2
[3]
Paul Foulkes, Gerry Docherty, Stefanie Shattuck Hufnagel, and Vincent Hughes
Linguistics Vanguard, 2018, Volume 4, Number s2

Comments (0)

Please log in or register to comment.
Log in