The aim of this study is to determine the effect of word clustering method on vocabulary learning of Iranian EFL learners through a case of semantic versus phonological clustering. To this effect, 80 homogeneous students from four intermediate classes at an English institute in Torbat e Heydariyeh participated in this research. They were assigned to four groups according to semantic versus phon...

The use of clustering strategies in semantic and phonological fluency was tested in children in third (age 8-9) and fifth (age 10-11) grades. To analyze the development of clustering strategies in the fluency task, the latency to the first word and intervals between clustered and non-clustered words were recorded. Semantic fluency was greater than phonological fluency in both age groups. Childr...

An HMM-based speech synthesis framework is applied to both Standard Austrian German and a Viennese dialectal variety and several training strategies for multi-dialect modeling such as dialect clustering and dialect-adaptive training are investigated. For bridging the gap between processing on the level of HMMs and on the linguistic level, we add phonological transformations to the HMM interpola...

We test a method of clustering dialects of English according to patterns of shared phonological features. Previous linguistic research has generally considered phonological features as independent of each other, but context is important: rather than considering each phonological feature individually, we compare the patterns of shared features, or Mutual Information (MI). The dependence of one p...

Dialects can be categorized in many ways. Using external features, dialects may be grouped by geographic location (e.g., Irish English), ethnic identity (e.g., AAVE), or social networks (e.g., Liberian Settler English) of their speakers. Or, using internal features, dialects may be grouped by shared features of pronunciation, vocabulary, or grammar. We explore quantitative approaches to see how...

The goal of this paper is to present a new method to automatically generate pronunciation rules for automatic segmentation of speech the German MAUSER system. MAUSER is an algorithm which generates pronunciation rules independently of any domain dependent training data either by clustering and statistically weighting self-learned rules according to a small set of phonological rules clustered by...

In this paper, we present an approach to automatically revealing phonological classes within historically related languages. A newly created bilingual German-Dutch pronunciation dictionary is used for learning phonological similarities between the onsets, nuclei and codas of these two languages via EM-based clustering. Our evaluation is twofold: we apply the models to predict from a German word...

In this paper, we present an approach to automatically revealing phonological classes within historically related languages. A newly created bilingual German-Dutch pronunciation dictionary is used for learning phonological similarities between the onsets, nuclei and codas of these two languages via EM-based clustering. Our evaluation is twofold: we apply the models to predict from a German word...