This article discusses a sociocultural usage-based perspective on the development of sociolinguistic competence. Previous research has focused on learners’ acquisition and use of alternative ways of ‘saying the same thing’ (i.e. native-like variation) in relation to study abroad, contact with native speakers, and pedagogy. Missing from the literature are studies examining the developmental trajectories of individual learners from a qualitative perspective. This article takes a first step in this direction by documenting the specific lexicogrammatical constructions deployed by one learner of French, Leon, over the course of a 6-week stylistic variation intervention. The findings show that his sociolinguistic repertoire emerged from a single multiword expression, which in combination with Leon’s application of new metalinguistic knowledge and mediation from a teacher expanded to become a more productive schematic template. The research suggests that future work on L2 sociolinguistic development would do well to focus on qualitative accounts of individual developmental trajectories, emphasizing the specific lexicogrammatical constructions learners appropriate, to understand how L2 sociolinguistic repertoires are constructed across time.

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In this paper, we investigate the use of the total likelihood (the weighted sum of the likelihoods of all possible state sequences) instead of the approximation with the Viterbi likelihood (the likelihood of the best state sequence) normally used in speech recognition. Next to its use in a recognizer, the use of total likelihoods in the context of an automatic word alignment task is also addressed shortly. We describe how the search algorithm must be modified and how word lattices based on total likelihoods can be constructed. The total likelihood framework also requires us to make a distinction between upgrading the language model scores or downgrading the acoustic model scores in the recognizer. To help in deciding between these two alternatives, some theoretical foundation is given to the practice of making a weighted combination of language and acoustic scores. Finally, the total likelihood and the Viterbi framework are compared in terms of accuracy and computational effort on the Wall Street Journal recognition task, while the accuracy of word alignments is evaluated on a large Dutch corpus.