Accounting for lexical tones when modeling phonological distance
Bibliographic Data
| ID | 3028113 |
|---|---|
| Authors | Youngah Do (0000-0002-9348-0351), Ryan Ka Yau Lai (0000-0001-6478-0649) |
| Year | 2021 |
| Volume | 97 |
| Issue | 1 |
| Pages | e39-e67 |
| Publication date | 2021-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language (JOURNAL) |
| Journal identifiers | ISSN: 0097-8507 • E-ISSN: 1535-0665 |
| Publisher | Project MUSE (PUBLISHER • US) |
| DOI | 10.1353/lan.2021.0008 |
| OpenAlex | W3149794542 |
| Language | EN |
| Citations received | 4 |
| References cited | 21 |
Methods of quantifying distance between sound sequences are known as phonological distance measures. Despite the wide application across subfields, phonological distance has been calculated mainly with features related to consonants and vowels. This research report establishes new measurements of phonological distance that incorporate lexical tone through experimental approaches and modeling, using Hong Kong Cantonese as a case study. Results show correspondences between the experimental data and predictions from information-theoretic measures, including entropy measures and functional load, suggesting that lexical components which play a more crucial role in phonological distance judgments are lexically less predictable as well. Implications for phonological distance measures are discussed
Distance measures · Linguistics · Natural language processing · Phonological rule · Phonology · Speech recognition · Tone (literature) · Artificial Intelligence · Computer Science · Mathematics · Phonetics and Phonology Research · Speech and Audio Processing · Speech Recognition and Synthesis
Data Analysis Using Regression and Multilevel/Hierarchical Models
Linear Statistical Inference and its Applications
Modern Cantonese Phonology
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A course in phonetics
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Inducing a measure of phonetic similarity from pronunciation variation
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Statistical methods for linguistic research
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| Citations per year | 0.67 |
|---|---|
| Citation span | 2020 - 2026 (7) |
| Highly cited | No |