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Cross-linguistic comparison of rhythmic and phonotactic similarity
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|Title:||Cross-linguistic comparison of rhythmic and phonotactic similarity|
|Issue Date:||Dec 2013|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [December 2013]|
|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 on properties 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.
|Description:||Ph.D. University of Hawaii at Manoa 2013.|
Includes bibliographical references.
|Appears in Collections:||Ph.D. - Linguistics|
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