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«CROSS-LINGUISTIC COMPARISON OF RHYTHMIC AND PHONOTACTIC SIMILARITY A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I ...»

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CROSS-LINGUISTIC COMPARISON OF RHYTHMIC AND PHONOTACTIC

SIMILARITY

A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION

OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

LINGUISTICS

DECEMBER 2013 By Diana Stojanović

Dissertation Committee:

Ann M. Peters, Chairperson Patricia Donegan Victoria Anderson Kamil Ud Deen Kyungim Baek © 2013, DIANA STOJANOVIĆ ii

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to all who provided inspiration, guidance, help, love, and support during my journey at the Department of Linguistics.

Members of my dissertation committee Professors Ann M. Peters, Patricia J. Donegan, Victoria B. Anderson, Kyungim Baek and Kamil Ud Deen;

All professors at the Department of Linguistics and in particular Byron Bender, Bob Blust, Mike Forman, William O’Grady, Ken Rehg, Albert Shutz, David Stampe, Ben Bergen, Katie Drager, Luca Onnis, Yuko Otsuka, and Amy Schafer;

Department secretaries who made impossible possible: Jen Kanda and Nora Lum;

Classmates and officemates: among many, Kaori Ueki, Yumiko Enyo, Gabriel Correa, Karen Huang, Laura Viana, Tatjana Ilic, Maria Faehndrich, Kathreen Wheeler, and Mie Hiramoto;

East-West Center and in particular Prof. Andrew Mason;

Udacity for teaching me enough Python to support this dissertation;

EWCPA and wonderful neighbors in Hale Kuahine;

Graduate Division, GSO, ISS, and in particular Martha Stuff and Linda Duckworth;

Family away from home Nelda Peterson, Christobel Sanders, Nina, Jo, and Kano;

My wonderful friends: among many, Bosiljka Pajic, Jadranka Bozinovska, Milka Smiljkovic, Svetlana Stanojevic, Aleksandra Petrovic, Branko Stojkovic, Ljiljana Milenkovic, Olga Jaksic, Jelena and Kosta Ilic, Helen Saar, Ange Nariswari, Ina Sebastian, Yoko Sato, and Parichat Jungwiwattanaporn;

My family and in particular my grandparents who spoke different languages and instilled the love for language in me; and my parents who supported me unconditionally;

And my dear husband Turro Wongkaren:

THANK YOU iii ABSTRACT Literature on speech rhythm has been focused on three major questions: whether languages have rhythms that can be classified into a small number of types, what the criteria are for the membership in each class, and whether the perceived rhythmic similarity between languages can be quantified based onproperties found in the speech signal.

Claims have been made that rhythm metrics – simple functions of the durations of vocalic and consonantal stretches in the speech signal – can be used to quantify rhythmic similarity between languages. Despite wide popularity of the measures, criticisms emerged stating that rhythm metrics reflect differences in syllable structure rather than rhythm.

In this dissertation, I first investigate what kind of similarity is captured via rhythm metrics. Then, I examine the relationship between the assumed rhythm type and the language structural complexity measured by the distributions of 1) consonant-cluster sizes, 2) phonotactic patterns, and 3) word lengths. Materials on which the measures of structural complexity were computed were automatically transcribed from written texts in 21 test languages. The transcriber is implemented in Python using grapheme-to-phoneme rules and simple phonological rules. Complexity measures are calculated using a set of functions, components of the complexity calculator.

Results show that several rhythm metrics are strongly correlated with the phonotactic complexity. In addition, linear relationship found between some metrics suggests that the information they provide is redundant. These results corroborate and extend results in the literature and suggest that rhythmic similarity must be measured differently. Structural similarity in many cases points to historical language grouping. Similarity of word-final clusters arises as a factor that most resembles rhythmic classification, although a large body of independent evidence of rhythmic similarity is necessary in order to establish this correspondence with more certainty.

Based on the results in this dissertation and the literature, a possible model of rhythmic similarity based on feature comparison is discussed, juxtaposing the current model based on rhythm metrics. This new ‘Union of features’ model is argued to better fit the nature of rhythm perception.

iv TABLE OF CONTENTS

Acknowledgements

Abstract

List of tables

List of figures

CHAPTER 1: INTRODUCTION

1.1 Rhythm correlates

1.2 Rhythm class hypothesis (RCH)

1.3 Rhythm metrics

1.4 Issues present in the current literature

1.5 Questions and approaches used to solve them

1.6 Contribution of this Dissertation

1.7 Outline

CHAPTER 2: BACKGROUND

2.1 Durational variability in speech





2.2 Phonotactics: Sonority scale and markedness of consonant clusters.............20 CHAPTER 3: METHODS

3.1 Model of the transcriber & the phonotactic calculator

3.2 Raw data assembly

3.3 Creating phonemic corpora

3.3.1 Choice of grapheme-to-phoneme method

3.3.2 Implementation of grapheme-to-phoneme method

3.4 Complexity calculator

3.4.1 Photactic metrics and rhythm metrics

3.4.2 Consonant-cluster measures

3.4.3 Word-length measures

vCHAPTER 4: RESULTS

4.1 Phonotactic component of Rhythm Metrics

4.1.1 Introduction

4.1.2 Correlations between the Phonotactic and Rhythm Metrics

4.1.3 Classification power of RMs and PMs

4.1.4 Language classification based on Phonotactic Metrics

4.1.5 Conclusion

4.2 Consonant cluster lengths at different positions in the word

4.2.1 Word-initial cluster distributions

4.2.2 Word-final cluster distributions

4.2.3 Word-medial cluster distributions

4.2.3 Summary

4.3 Phonotactic patterns at different positions in the word

4.3.1 Basic sonority (ALT) level

4.3.2 Detailed sonority (saltanajc) level

4.4 Word length distributions

4.5 Variability of measures over different materials

CHAPTER 5: GENERAL DISCUSSION AND CONCLUSION

5.1 Summary

5.2 Overview

5.3 Limitations of the study

5.4 Discussion

5.4.1 Additional questions

5.4.2 Use of modified speech in addressing questions on rhythmic similarity

5.4.3 The nature of rhythm

5.4.4 Proposed model of rhythmic similarity

5.4.5 Implications/prediction for L2 speech and learning in infants..................102 5.5 Conclusion

viAPPENDICES

Appendix 1: Basic properties of the languages from WALS

Appendix 2: Texts and transcripts for 21 languages

Appendix 3: Values of Rhythm and Phonotactic Metrics

Appendix 4: Word-length distributions for 21 languages

BIBLIOGRAPHY

–  –  –

Table 4.1 Correlation between the consonantal PMs and RMs

Table 4.2 Correlation between the vocalic PMs and RMs (long=1)

Table 4.3 Correlation between the vocalic PMs and RMs (long=2)

Table 4.4 Distribution of word-initial consonant clusters

Table 4.5 Distribution of word-final consonant clusters

Table 4.6 Distribution of word-medial consonant clusters

Table 4.7 Distribution of word-medial consonant clusters (re-arranged)

Table 4.8 Language groupings based on word-initial, word-medial, and word-final complexity

Table 4.9 Word-initial length-0 and length-1 clusters

Table 4.10 Word initial length-2 clusters

Table 4.11 Word-initial length-3 clusters

Table 4.12 Word-final length-0 and length-1 clusters

Table 4.13 World-final length-2 clusters

Table 4.14 Word-final clusters grouped by sonority

Table 4.15 World-final length-3 clusters

Table 4.16 Saltanajc frequencies in 21 languages

Table 4.17 Clusters of length-2 in ‘saltanajc’ scale: initial position

Table 4.18 Clusters of length-2 in ‘saltanajc’ scale: initial position

Table 4.19 Clusters of length-2 in ‘saltanajc’ scale: final position

Table 4.20 Clusters of length-2 in ‘saltanajc’ scale: word-final position

Table 4.21 Clusters of length-2 in ‘saltanajc’ scale: word-medial position

Table 4.22 Variability of phonotactic metrics over different texts

Table 4.23 Variability of consonant cluster complexity in word-initial position.

...........84 Table 4.24 Variability of consonant cluster complexity in word-final position..............84 Table 4.25 Variability of consonant cluster complexity in word-final position..............85 Table 4.26 Variability of word-length distribution: word tokens

Table 4.27 Variability of word-length distribution: lexical items

Table A1.1 Phonological properties of test-languages

Table A1.2 Morphological properties of test-languages

viii Table A3.1 Rhythm Metrics values

Table A3.2 Phonotactic Metrics values (long V = 1)

Table A3.3 Phonotactic Metrics values (long V = 2)

ix LIST OF FIGURES

Figure 2.1 Sonority scale (Vennemann 1988)

Figure 2.2 saltanaj sonority scale

Figure 2.3 ALT sonority scale

Figure 2.4 Preferred initial double clusters (Dziubalska-Kołaczyk 2001)

Figure 2.5 Preferred medial double clusters (Dziubalska-Kołaczyk 2001)

Figure 2.6 Preferred final double clusters (Dziubalska-Kołaczyk 2001)

Figure 3.1 Model of the transcriber

Figure 3.2 Model of the complexity calculator

Figure 3.3 Example: cluster distribution at ALT level

Figure 3.4 Example: cluster distribution at saltanajc level

Figure 4.1 Correlation between phonotactic (%Vp) and rhythmic (%Vr) percentage of vocalic intervals

Figure 4.2 Correlation between phonotactic (∆Cp) and rhythmic (∆Cr) standard deviation of consonantal intervals

Figure 4.3 Correlation between phonotactic (∆Vp) and rhythmic (∆Vr) standard deviation of vocalic intervals

Figure 4.4 Correlation between phonotactic (Varco-Cp) and rhythmic (Varco-Cr) coefficient of variation of consonantal intervals

Figure 4.5 Correlation between phonotactic (Varco-Vp) and rhythmic (Varco-Vp) coefficient of variation of vocalic intervals

Figure 4.6 Correlation between phonotactic (nPVI-Vp) and rhythmic (nPVI-Vr) normalized pair-wise variability index of vocalic intervals

Phonotactic metrics graph (%Vp, ∆Cp)

Figure 4.7 Rhythm metrics graph (%Vr, ∆Cr)

Figure 4.8 Figure 4.

9 Rhythm metrics graph (rPVI-Cr, nPVI-Vr)

Figure 4.10 Phonotactic metrics graph (rPVI-Cp, nPVI-Vp)

Figure 4.11 Grouping of 21 languages based on phonotactic %Vp and ∆Cp

Figure 4.12 Linear relationship between %Vp and ∆Cp

x Figure 4.13 Grouping of 21 languages based on phonotactic metrics rPVI-Cp and nPVI-Vp

Figure 4.14 Grouping of 21 languages based on phonotactic %Vp and Varco-Vp.

.........49 Figure 4.15 Distribution of word-final clusters based on sonority

Figure 4.16 Distribution of word-lengths: lexical items

Figure 4.17 Distribution of word-lengths: word tokens

Figure 4.18 Average word-length: word tokens

Figure 4.19 Average word-length: lexical items

Figure 4.20 Stability: Distribution of word lengths (lexical items)

Figure 4.21 Stability: Distribution of word-lengths (word tokens)

Figure 5.1 An example of a characteristic prosodic sequence

Figure A4.1 Distribution of word lengths for Bulgarian

Figure A4.2 Distribution of word lengths for Catalan

Figure A4.3 Distribution of word lengths for Czech

Figure A4.4 Distribution of word lengths for Dutch

Figure A4.5 Distribution of word lengths for Estonian

Figure A4.6 Distribution of word lengths for German

Figure A4.7 Distribution of word lengths for Greek

Figure A4.8 Distribution of word lengths for Hawaiian

Figure A4.9 Distribution of word lengths for Hungarian

Figure A4.10 Distribution of word lengths for Indonesian

Figure A4.11 Distribution of word lengths for Italian

Figure A4.12 Distribution of word lengths for Japanese

Figure A4.13 Distribution of word lengths for Maori

Figure A4.14 Distribution of word lengths for Polish

Figure A4.15 Distribution of word lengths for Portuguese

Figure A4.16 Distribution of word lengths for Russian

Figure A4.17 Distribution of word lengths for Samoan

Figure A4.18 Distribution of word lengths for Serbian

Figure A4.19 Distribution of word lengths for Spanish

Figure A4.20 Distribution of word lengths for Tongan

–  –  –

Rhythm of speech has been vigorously discussed during the last century and it continues to be a topic of research in phonetics, phonology, language acquisition, and the similarity between speech and music. In linguistics literature, research on rhythm was first seen in description and analysis of poetry and rhythmic meter. The topic gained controversial status following Pike’s 1945 observation that English and French differ in their rhythms, describing the former as more Morse code-like and the latter machine-gun-like. Pike’s statement – however misunderstood, as he actually does not juxtapose them but says English has possibly two rhythms, one resembling French (Pike 1945) – has been generally interpreted as a dichotomy of two types of rhythms, which led to an even stronger claim by Abercrombie (1967) that all spoken languages must be of one or the other type. At this time, only a small subset of world languages was considered, so this conjecture was indeed very brave.



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