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Robustness and Complexity in Italian Mid Vowel Contrasts

Datos Bibliográficos

ID5901151
AutoresMargaret E L Renwick (0000-0002-8759-1717, University of Georgia, autor de correspondencia)
Año2024
Volumen9
Número4
Páginas150
Fecha de publicación2024-04-18
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaLanguages (JOURNAL)
Identificadores de la revistaISSN: 2226-471X • E-ISSN: 2226-471X
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/languages9040150
OpenAlexW4394913334
IdiomaEN
Referencias citadas54

Accounts of phonological contrast traditionally invoke a binary distinction between unpredictable lexically stored phonemes and contextually predictable allophones, whose patterning reveals speakers' knowledge about their native language. This paper explores the complexity of contrasts among Italian mid vowels from a multifaceted perspective considering the lexicon, linguistic structure, usage, and regional variety. The Italian mid vowels are marginally contrastive due to a scarcity of minimal pairs alongside variation in phonetic realization. The analysis considers corpus data, which indicate that the marginal contrasts among front vowels vs. back vowels are driven by different sources and forces. Functional loads are low; while front /e ɛ/ have the weakest lexical contrast among all Italian vowels, back /o ɔ/ are separated by somewhat more minimal pairs. Among stressed front vowels, height is predicted by syllable structure and is context-dependent in some Italian varieties. Meanwhile, the height of back mid vowels is predicted by lexical frequency, in line with expectations of phonetic reduction in high-frequency contexts. For both front and back vowels, the phonetic factor of duration predicts vowel height, especially in closed syllables, suggesting its use for contrast enhancement. The results have implications for a proposed formalization of Italian mid vowel variation

Biology · Robustness (evolution · Speech recognition · Vowel · Computer Science · Linguistic Variation and Morphology · Phonetics and Phonology Research · Speech Recognition and Synthesis

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