We report on a series of experiments with probabilistic context-free grammars predicting English and German syllable structure. The treebank-trained grammars are evaluated on a syllabification task. The grammar used by Müller (2002) serves as point of comparison. As she evaluates the grammar only for German, we reimplement the grammar and experiment with additional phonotactic features. Using bi-grams within the syllable, we can model the dependency from the previous consonant in the onset and coda. A 10fold cross validation procedure shows that syllabification can be improved by incorporating this type of phonotactic knowledge. Compared to the grammar of Müller (2002), syllable boundary accuracy increases from 95.8% to 97.2% for English, and from 95.9% to 97.2% for German. Moreover, our experiments with different syllable structures point out that there are dependencies between the onset on the nucleus for German but not for English. The analysis of one of our phonotactic grammars shows that interesting phonotactic constraints are learned. For instance, unvoiced consonants are the most likely first consonants and liquids and glides are preferred as second consonants in two-consonantal onsets.

In this paper, we present an approach to automatically revealing phonological correspondences within historically related languages. We create two bilingual pronunciation dictionaries for the language pairs German-Dutch and German-English. The data is used for automatically learning phonological similarities between the two language pairs via EMbased clustering. We apply our models to predict from a phonological German word the phonemes of a Dutch and an English cognate. The similarity scores show that German and Dutch phonemes are more similar than German and English phonemes, which supplies statistical evidence of the common knowledge that German is more closely related to Dutch than to English. We assess our approach qualitatively, finding meaningful classes caused by historical sound changes. The classes can be used for language learning.