Substantive Bias and Natural Classes
An Empirical Approach
This book offers a laboratory phonological analysis of the sonority hierarchy and natural classes in nasal harmony using an artificial grammar-learning paradigm. It is aimed at postgraduate students and linguists in general whose research interests lie in phonology, phonetics, and/or psycholinguistics. It is useful for linguists who are struggling to figure out how to effectively design an artificial phonological grammar and those who have not designed experiments on their own but would like to do so as an additional means to testing linguistic theories. This book is also a valuable resource for anyone building crosslinguistic artificial gra…
Mehr
CHF 122.00
Preise inkl. MwSt. und Versandkosten (Portofrei ab CHF 40.00)
V301:
Libri-Titel folgt in ca. 2 Arbeitstagen
Produktdetails
- ISBN: 978-981-1335-33-4
- EAN: 9789811335334
- Produktnummer: 29343593
- Verlag: Springer-Verlag GmbH
- Sprache: Englisch
- Erscheinungsjahr: 2019
- Seitenangabe: 122 S.
- Masse: H24.1 cm x B16.0 cm x D1.3 cm 383 g
- Abbildungen: Book; 6 schwarz-weiße Abbildungen, Bibliographie
- Reihenbandnummer: 8
- Gewicht: 383
Über den Autor
?Yu-Leng Lin is an Assistant Professor in the Department of Foreign Languages and Literature at the Feng Chia University. She received her PhD degree from the Department of Linguistics at the University of Toronto in 2016. Before joining the Feng Chia University, she served as a postdoctoral fellow in the Hong Kong Polytechnic University's Department of Chinese and Bilingual Studies. Her research interests include psycholinguistics, Chinese linguistics, laboratory phonology, and sociophonetics. Her publications include a journal paper, a book chapter and conference proceedings, and she has presented her work - ranging from learning bias, speech perception and production, tonal studies, and comparative studies among Mandarin, Taiwan Southern Min, Cantonese, and English - at several international conferences.
1 weiteres Werk von Yu-Leng Lin:
Bewertungen
Anmelden