Modeling Variation in Taiwan Southern Min Syllable Contraction

台灣閩南語音節連併變化之模型建構

本文嘗試運用一種推測性優選理論模型，即為漸進學習演算法（the Gradual Learning Algorithm），來建構臺灣閩南語音節連併的變化。為探測此模型的有效性，三種複雜度互異的資料因而置入模型：第一種類型為符合許（2003）分析的完全連併音節；第二種類型為除了前項類型之外，增加了許（2003）分析的特例，但其仍為以閩南語為母語者認可的完全連併音節；第三種類型取自李（2005）語言發聲實驗的半連併音節，最為突顯語音的變化。結果顯示此模型能夠在置入不同資料後，分別提供合理的制約排列順序，同時能夠涵括其一般性及變化性。因此，本文證實推測性優選理論模型似乎能夠建構符合語言事實的語法。

In this paper we attempt to model variation in Taiwan Southern Min syllable contraction using the Gradual Learning Algorithm (GLA; Boersma and Hayes 2001), an Optimality-Theoretic model with variable constraint ranking. To explore the effectiveness of GLA, we look at three data sets of increasing complexity: non-variable fully contracted forms as analyzed by Hsu (2003), variable outputs as noted by Hsu and confirmed by other native speakers, and phonetically variable outputs collected in a speech production experiment by Li (2005). The results reveal that GLA is capable of providing plausible constraint ranking hierarchies that capture both major generalizations and variability. Stochastic constraint evaluation thus seems to be a promising mechanism in the construction of grammars.