Quantifying Rhythm in Running Speech

1. UCSD Phonetics Lab UCSD Phonetics Lab Quantifying Rhythm in Running Speech Tristie A. Ross Naja Ferjan Amalia Arvaniti University of California, San Diego

2. Rhythmic Categories • Lloyd (1940) mentioned “morse-like” and “machine gun” rhythm in Speech Signals and Telephony • Pike (1945) developed this into the notion of stress timing and syllable timing in The Intonation of American English • Syllable timing: equal time given to each syllable • Stress timing: equal time between stresses • Abercrombie (1967) proposed that all languages fall into one of these two rhythmic categories • Dauer (1983, 1987) proposed that instead of categories languages form a rhythm continuum, defined on the basis of certain phonetic and phonological criteria, including the durational variability of consonantal and vocalic intervals

3. Quantifying Speech Rhythm • In the past decade Dauer’s proposal has been applied to the development of a number of metrics said to reflect linguistic rhythm • %V, ΔC (Ramus, Nespor & Mehler 1999) • nPVI, rPVI (Grabe & Low 2002) • VarcoV, VarcoC (White & Mattys 2007) • These metrics are based on the idea that rhythm type depends on consonantal and vocalic variability • Stress-timed languages are expected to show larger consonantal variability and a smaller proportion of (more variable) vocalic intervals relative to syllable-timed languages

4. Hypotheses • Metric scores are likely to be affected by materials used; if so, more “stress-timed” materials should yield more stress-timed scores • Metrics are likely to vary across speakers; if so, larger numbers of speakers should yield more meaningful results • Even with additional speakers, unclassified languages may remain resistant to classification using rhythm metrics • Differences in scores may also be related to style and speaking rate; if so, read sentences may present different scores than running spontaneous speech

5. Methods: Speakers & Materials • Languages • Southern Californian English –Stress-timed (1 M, 2 F) • Standard Northern German –Stress-timed (1 M, 5 F) • Standard Italian –Syllable-timed (5 F) • Standard Latin American Spanish –Syllable-timed (1 M, 2 F) • Standard Korean –Syllable-timed?(1 M, 2 F) • Standard Greek –Mixed rhythm (1 F) • Speakers • 18-36 year olds, recruited from the UCSD community • All speakers had lived in the US for less than 3 years • Materials • 15 read sentences; 5 “stress-timed”, 5 “syllable-timed”, 5 uncontrolled • Read running speech: The North Wind and the Sun • Spontaneous Speech: 1-2 minutes

6. Methods: Measurements • Measurements of consonantal and vocalic intervals were made on the basis of phonetic criteria • Phrase-final intervals were not excluded • Pauses were excluded from measurement • Glides were included in vocalic intervals if they showed no evidence of frication or were included in consonantal intervals if there was evidence of frication

7. Methods: Quantitative Analysis • Metrics • %V, ΔC • nPVI, rPVI • VarcoV, VarcoC • Statistics • One-way between-subjects ANOVAs with LANGUAGE as a categorical variable and the metric scores for each language as dependent variables • One-way ANOVAs for each language with SENTENCE-SUBSETas a repeated-measures factor and the metric scores for each subset within that language as dependent variables • p < 0.05 whenever a difference between scores is reported

8. Results: ΔC & %V • Italian, Korean and Spanish are visually separated from German and English and in the “syllable-timed” region • However, the only significant difference is between German %V and all other languages

9. Results: rPVI & nPVI • English and German appear to be separated from Italian, Korean and Spanish, but no differences are statistically significant

10. Results: VarcoV & VarcoC • The languages are not clearly separated into groups • German appears “misplaced” • Only statistical difference: English has higher VarcoC than all other languages except Italian

11. Results: Comparing Metrics i. • The metrics do not give similar patterns for all the languages • VarcoC shows overall smaller differences in consonantal variability than ΔC and rPVI and a slightly different pattern for German • VarcoV shows minimal differences in vocalic variability across languages

12. English Results: Comparing Metrics ii. EnglishGermanItalianKoreanSpanish %V ΔC Varcos PVIs

13. English Results: Comparing Metrics iii. EnglishGermanItalianKoreanSpanish Varcos PVIs

14. Results: the role of materials • Our data also show that the choice of materials can significantly affect scores and language classification • Some languages are affected more than others • geminates in Italian materials can significantly change scores of consonantal variability • despite differences, the scores of all languages were affected by the choice of materials

15. Results: Italian ΔC and %V • “Syllable-timed” materials yielded significantly different scores • These scores were more syllable-timed than those of “stress-timed” and “uncontrolled” materials

16. Results: English %V, Varco C & rPVI • %V is significantly lower for “stress-timed” materials than “syllable-timed” • Consonantal variability is significantly different for “stress-timed” materials, but different metrics show opposite directions for this trend

17. Results: German %V & VarcoV • “Stress-timed” materials yielded significantly different vocalic scores than the other two subsets • %V suggests that the “stress-timed” materials are more stress-timed than the rest • But VarcoV suggests that they are less stress-timed

18. Results: Korean %V • “Stress-timed” materials show a significantly lower percentage of vocalic intervals than uncontrolled materials

19. Results: Spanish VarcoV • Uncontrolled materials show significantly more vocalic variability than either “stress-timed” or “syllable-timed” materials

20. Discussion & Conclusions • Our data do not show clear evidence for a rhythmic dichotomy (advocated by some, e.g. Ramus et al. 1999) • They also cast doubt on the presence of a continuum onto which languages can be placed in a straightforward manner • Most of the differences in scores between languages were not statistically significant • Languages were placed in different parts of the metric space depending on the metrics used • Crucially, the choice of materials can significantly affect language placement, although different metrics gave contradictory results • Our results overall cast doubt on the robustness of the metrics and their effectiveness in quantifying linguistic rhythm

21. THANK YOU!!! tross@ling.ucsd.edunaja@ling.ucsd.eduamalia@ling.ucsd.edu

22. Methods: Materials • “stress-timed” • The production increased by three fifths in the last quarter of 2007. • El ingeniero siempre parecía bastante amable. • “syllable-timed” • Lara saw Bobby when she was on the way to the photocopy room. • La casa de la profesora no parece pequeña. • Uncontrolled • It was nine o’clock when we finished breakfast and went out on the porch. • Las oficinas estaban cerradas y a oscuras por el día feriado.

23. References • Abercombie, D. (1967) Elements of general phonetics. Chicago: Aldine. • Dauer, R. (1983) Stress-timing and syllable-timing reanalyzed. Journal of Phonetics, 11, 51-62. • Dauer, R. (1987) Phonetic and phonological components of language rhythm. In Proceedings of the 11th international congress of phonetic sciences, 447-450. • Grabe, E., Low, E. L. (2002) Durational variability in speech and the rhythm class hypothesis. Papers in Laboratory Phonology 7, 515-546. Berlin: Mouton. • Lloyd, J. (1940) Speech signals in telephony. London: Pitman & Sons. • Pike, K. (1945) The intonation of American English. Ann-Arbor: University of Michigan Press. • Ramus, F., Nespor, M., Mehler, J. (1999) Correlates of linguistic rhythm in the speech signal. Cognition, 72, 1-28. • White, L., Mattys S. L. (2007) Calibrating rhythm: First and second language studies. Journal of Phonetics, 35, 501-522.