The Measurement of Functional Load

Wang, William Yang

doi:10.1159/000258556

Cited by 22 publications

(18 citation statements)

References 6 publications

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“…The main difference between King's (1967b) formulation and those of other writers is that it is based on conditional probabilities instead of being an information theory approach. Wang (1967; see also Wang & Thatcher, 1962) compares four information theory measures of functional load, including those of Greenberg (1959) and Hockett (1955;see also Hockett, 1966), by considering the extent to which they fulfill five conditions: (a) that we should be able to calculate the functional load of any set of phonemes in a language, (b) that the functional load of a set containing only one phoneme is zero, (c) that the phonemes of a set whose functional load is zero are in complementary distribution, (d) that the functional load of the union of two nonoverlapping sets is greater than or equal to the sum of their individual functional loads, and (e) that the functional load of a set is equal to the uncertainty of the language if the set corresponds to the entire phonemic system.…”

Section: Definitions Of Functional Loadmentioning

confidence: 99%

Functional Load and the Teaching of Pronunciation

Brown¹

1988

TESOL Quarterly

137

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The concept of functional load has been used by various writers in various linguistic fields and has consequently received differing definitions and methods of calculation. It has not, however, been applied to the teaching of pronunciation. This article examines several aspects of functional load in English that may be relevant for assessing the relative importance of segmental features of learners' speech. Implications for the use of pronunciation drill books are discussed.

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Section: Definitions Of Functional Loadmentioning

confidence: 99%

Functional Load and the Teaching of Pronunciation

Brown¹

1988

TESOL Quarterly

137

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“…As illustrated in Fig 1B, the start and end of a significant motif are marked by sudden drops in their rightward and leftward extension probabilities. There also is precedence of using such extension probabilities to analyze transitions in phonological linguistic elements in [69, 70]. The drop ratios are defined as and When D < η , the drop threshold cutoff, we consider the sub-sequence a significant motif.…”

Section: Methodsmentioning

confidence: 99%

Functional shortcuts in language co-occurrence networks

2018

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Human language contains regular syntactic structures and grammatical patterns that should be detectable in their co-occurence networks. However, most standard complex network measures can hardly differentiate between co-occurence networks built from an empirical corpus and a body of scrambled text. In this work, we employ a motif extraction procedure to show that empirical networks have much greater motif densities. We demonstrate that motifs function as efficient and effective shortcuts in language networks, potentially explaining why we are able to generate and decipher language expressions so rapidly. Finally we suggest a link between motifs and constructions in Construction Grammar as well as speculate on the mechanisms behind the emergence of constructions in the early stages of language acquisition.

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“…We propose to merge the segments with low functional load [44], [17] , which is defined as the segment contrasts' work to differentiate utterances [21], [45], [46], [47]. Segments with low functional load can usually be disambiguated by their surroundings and convey little information [45].…”

Section: Functional Load and Economical Principlementioning

confidence: 99%

Tackling Perception Bias in Unsupervised Phoneme Discovery Using DPGMM-RNN Hybrid Model and Functional Load

Sakti

Zhang

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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The human perception of phonemes is biased against speech sounds. The lack of correspondence between perceputal phonemes and acoustic signals forms a big challenge in designing unsupervised algorithms to distinguish phonemes from sound. We propose the DPGMM-RNN hybrid model that improves phoneme categorization by relieving the fragmentation problem. We also merge segments with low functional load, which is the work done by segment contrasts to differentiate between utterances, just like humans who convert unambiguous segments into phonemes as units for immediate perception. Our results show that the DPGMM-RNN hybrid model relieves the fragmentation problem and improves phoneme discriminability. The minimal functional load merge compresses a segment system, preserves information and keeps phoneme discriminability.

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The Measurement of Functional Load

Cited by 22 publications

References 6 publications

Functional Load and the Teaching of Pronunciation

Functional Load and the Teaching of Pronunciation

Functional shortcuts in language co-occurrence networks

Tackling Perception Bias in Unsupervised Phoneme Discovery Using DPGMM-RNN Hybrid Model and Functional Load

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