Javascript must be enabled for the correct page display

Comparison of Dutch Dialects

Wieling, M.B. (2007) Comparison of Dutch Dialects. Master's Thesis / Essay, Computing Science.

Infor_Ma_2007_MBWieling.CV.pdf - Published Version

Download (3MB) | Preview


Contemporary Dutch dialects are compared using the most recent Dutch dialect source available: the Goeman-Taeldeman-Van Reenen-Project data (GTRP). The GTRP consists of phonetic transcriptions of 1876 items for 613 localities in the Netherlands and Belgium gathered during the period 1980 — 1995. In this study three different approaches will be taken to obtain dialect distances used in dialect comparison. In the first approach the GTRP is analysed using the Levenshtein distance as a measure for pronunciation difference. The dialectal situation it represents is compared to the analysis of a 350-locality sample from the Reeks Nederlands(ch)e Dialectatlassen (1925 — 1982) studied byHeeringa (2004). Due to transcriptional differences between the Netherlandic and Belgian GTRP data we analysedata from the two countries separately. The second approach consists of using Pair Hidden Markov Models to automatically obtain segment distances and to use these to improve the sequence distance measure. The improved sequence distance measure is used in turn to obtain better dialect distances. The results are evaluated in two ways, first via comparison to analyses obtained using the Levenshtein distance on the same datasets and second, by comparing the quality of the induced vowel distances to acoustic differences. In the final approach we propose two adaptations of the regular Levenshtein distance algorithm based on psycholinguistic work on spoken word recognition. The first adaptation follows the idea of the Cohort Model which assumes that the word-initial part is more important for word recognition than the word-final part. The second adaptation follows the idea that stressed syllables contain more information and are more important for word recognition than unstressed syllables. Both algorithms are evaluated by comparing them to the results using the regular Levenshtein distance on several data sets.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30

Actions (login required)

View Item View Item