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Automatic summarization systems usually are trained and evaluated in a particular domain with fixed data sets. When such a system is to be applied to slightly different input, labor- and cost-intensive annotations have to be created to retrain the system. We deal with this problem by providing users with a GUI which allows them to correct automatically produced imperfect summaries. The corrected summary in turn is added to the pool of training data. The performance of the system is expected to improve as it adapts to the new domain.
The effect of manipulation of a speaker’s voice as well as exposure to a native speaker’s utterance was investigated regarding the pronunciation of stops by German learners of French. Three subject groups, a Control (CG), a Manipulation (MG), and a Native Speaker (NG) Group, were recorded on two subsequent days. The MG was presented with a manipulation of their voice on the second day and the NG listened to a native French speaker, while the CG did not receive any feedback. Results show that speakers of the MG and NG were able to extract useful information from the respective feedback and successfully adapted to it. Participants were able to reduce their voice onset time values, although speakers of the NG reduced it to a greater extent.