En perceptuell och akustisk studie av svenskans koronaler i ett dialektperspektiv

Detta är en avhandling från Stockholm : Institutionen för Lingvistik, Stockholms universitet

Sammanfattning: The purpose of this dissertation is to contribute to a typology of the Swedish language as it was spoken in the entire Swedish speaking area around the year 2000. Specifically, the plan is to base this typology solely on different pronunciations of the four coronal consonants /t, d, n/ and /l/. The idea is then to draw borders between dialects based on the perceptual and acoustical properties of these different pronunciations and to compare these new borders between dialects with the ones drawn by traditional dialectology. As this is an experimental study it is of interest to see whether the measured data that has been acquired here will conflict with or support earlier typologies based on other methods. The idea is also to compare this work with other, modern typologies of the Swedish language and see if the borders drawn between dialects in this work are also present when totally different features of the language are examined. Two such typologies are for instance Shaeffler’s (2005) typology based on Swedish quantity data and Leinonen’s (2010) typology based on Swedish vowel quality data. The interesting thing then is to see if it is possible to recreate a map of the Swedish dialect distribution from data that is comparable to the picture of traditional dialectology. This dissertation shows that we can indeed make a kind of typology for the Swedish language based on the perceptual and acoustical properties of the four coronals /t, d, n/ and /l/ alone and that this typology corroborates some of the traditional dialectology findings but also reveals new regional distributions of sounds that puts the idea of what constitutes a dialect in a new and somewhat different perspective. It is also shown that the articulatorily motivated and acoustically verified method of analysis developed and used in this series of studies reliably can be used to analyse large bodies of language data like this.

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