Rank-Frequency Distributions for Phonemes

Sigurd, Bengt

doi:10.1159/000258595

Cited by 20 publications

(19 citation statements)

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“…It is genuinely a poor fit for the vast majority of languages. This result accords well with earlier work which has found that a simple, one-parameter form of the power law distribution poorly characterizes phoneme frequencies (Sigurd, 1968 ; Martindale et al, 1996 ; Tambovtsev and Martindale, 2007 ).…”

Section: Resultssupporting

confidence: 92%

Re-evaluating Phoneme Frequencies

Macklin-Cordes

Round

2020

Front. Psychol.

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Causal processes can give rise to distinctive distributions in the linguistic variables that they affect. Consequently, a secure understanding of a variable's distribution can hold a key to understanding the forces that have causally shaped it. A storied distribution in linguistics has been Zipf's law, a kind of power law. In the wake of a major debate in the sciences around power-law hypotheses and the unreliability of earlier methods of evaluating them, here we re-evaluate the distributions claimed to characterize phoneme frequencies. We infer the fit of power laws and three alternative distributions to 166 Australian languages, using a maximum likelihood framework. We find evidence supporting earlier results, but also nuancing them and increasing our understanding of them. Most notably, phonemic inventories appear to have a Zipfian-like frequency structure among their most-frequent members (though perhaps also a lognormal structure) but a geometric (or exponential) structure among the least-frequent. We compare these new insights the kinds of causal processes that affect the evolution of phonemic inventories over time, and identify a potential account for why, despite there being an important role for phonetic substance in phonemic change, we could still expect inventories with highly diverse phonetic content to share similar distributions of phoneme frequencies. We conclude with priorities for future work in this promising program of research.

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Section: Resultssupporting

confidence: 92%

Re-evaluating Phoneme Frequencies

Macklin-Cordes

Round

2020

Front. Psychol.

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“…Taken together, (24) and (25) imply that the clustering with respect to authors is better visible for frequencies extracted from different words of the texts (the inter-cluster distance increases, whereas the intra-cluster distance decreases). The same effect was obtained above via fitted values of β's; see (17). Table I and for the definition of ρ0 and ρ1.…”

Section: B Distance Between Phoneme Frequenciessupporting

confidence: 76%

“…Eqs. (17,18) compare the data presented in Tables II and III We studied 48 English texts written by 16 different, native-English authors; see Table I and section III of the supplementary material. For each text we extracted the phoneme frequencies {f r } n r=1 and ordered them as in (1); the list of English phonemes is given in section I of the supplementary material.…”

Section: Distribution Of Ordered Probabilities (Order Statistics)mentioning

confidence: 99%

Stochastic Model for Phonemes Uncovers an Author-Dependency of Their Usage

Deng

Allahverdyan

2016

PLoS ONE

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We study rank-frequency relations for phonemes, the minimal units that still relate to linguistic meaning. We show that these relations can be described by the Dirichlet distribution, a direct analogue of the ideal-gas model in statistical mechanics. This description allows us to demonstrate that the rank-frequency relations for phonemes of a text do depend on its author. The author-dependency effect is not caused by the author’s vocabulary (common words used in different texts), and is confirmed by several alternative means. This suggests that it can be directly related to phonemes. These features contrast to rank-frequency relations for words, which are both author and text independent and are governed by the Zipf’s law.

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“…In contrast, voiceless sib ilants (/s/, ///) are decisively favored over their voiced counterparts (Izl, l$l) | Wang and Craw ford. I960; Sigurd, 1968).…”

Section: The Voicing-sibilance Interactionmentioning

confidence: 99%

Some Distributional Facts about Fricatives and a Perceptual Explanation

Balise

Diehl

1994

Phonetica

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Across and within languages voiced sibilants tend to be disfavored relative to voiceless ones. This paper explores the claim that voicing more adversely affects the distinctive acoustic properties of sibilants than those of nonsibilants. One prediction associated with this claim is that voicing differentially lowers the amplitude of frication noise for sibilants and non-sibiliants so that amplitude differences between the two classes are reduced. Acoustic measurements confirm this prediction. A second prediction is that voicing has a greater negative effect on the identification of sibilants than nonsibilants. Perceptual results from this and previous studies are somewhat variable, but averaged data support this prediction. The findings suggest that voiced sibilants are disfavored in part for perceptual reasons.

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Rank-Frequency Distributions for Phonemes

Cited by 20 publications

References 1 publication

Re-evaluating Phoneme Frequencies

Re-evaluating Phoneme Frequencies

Stochastic Model for Phonemes Uncovers an Author-Dependency of Their Usage

Some Distributional Facts about Fricatives and a Perceptual Explanation

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