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Research into typing patterns has broad applications in both psycholinguistics and biometrics (i.e., improving security of computer access via each user's unique typing patterns). We present a new software package, TypingSuite, which can be used for presenting visual and auditory stimuli, collecting typing data, and summarizing and analyzing the data. TypingSuite is a Java-based software package that is platform-independent and open-source. To validate TypingSuite as a beneficial tool for researchers who are interested in keystroke dynamics, two studies were conducted. First, a behavioural experiment based on single word typing was conducted that replicated two well-known findings in typing research, namely the lexicality and frequency effects. The results confirmed that words are typed faster than pseudowords and that high frequency words are typed faster than low frequency words. Second, in regard to biometrics, it was also shown that typing data from the same user are more similar than data from different users. Because TypingSuite allows its users to easily implement an experiment and to collect and analyze data within a single software package, it holds promise for being a valuable educational and research tool in language-related sciences such as psycholinguistics and natural language processing.

Automatic syllabification of words is challenging, not least because the syllable is not easy to define precisely. Consequently, no accepted standard algorithm for automatic syllabification exists. There are two broad approaches: rule-based and data-driven. The rule-based method effectively embodies some theoretical position regarding the syllable, whereas the data-driven paradigm tries to infer "new" syllabifications from examples assumed to be correctly syllabified already. This article compares the performance of several variants of the two basic approaches. Given the problems of definition, it is difficult to determine a correct syllabification in all cases and so to establish the quality of the "gold standard" corpus used either to evaluate quantitatively the output of an automatic algorithm or as the example-set on which data-driven methods crucially depend. Thus, we look for consensus in the entries in multiple lexical databases of pre-syllabified words. In this work, we have used two independent lexicons, and extracted from them the same 18,016 words with their corresponding (possibly different) syllabifications. We have also created a third lexicon corresponding to the 13,594 words that share the same syllabifications in these two sources. As well as two rule-based approaches (Hammond's and Fisher's implementation of Kahn's), three data-driven techniques are evaluated: a look-up procedure, an exemplar-based generalization technique, and syllabification by analogy (SbA). The results on the three databases show consistent and robust patterns. First, the data-driven techniques outperform the rule-based systems in word and juncture accuracies by a very significant margin but require training data and are slower. Second, syllabification in the pronunciation domain is easier than in the spelling domain. Finally, best results are consistently obtained with SbA. (Contains 9 tables, 4 figures and 7 footnotes.)