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In this work we verify with accuracy that acoustically transcribed durations of linguistic units at several scales (phonemes, words and Breath Groups) comply with log-normal distribution. To do this we have used a very well-known Corpus which contains conversational speech by native English speakers gathering approximately 3•10^5 words with time-aligned phonetic labels. Secondly, we explain this log-normal distribution using a new model: a Non-interacting Cascade Approach (NICA) model. This NICA model can explain the emergence of Lognormal distributions across linguistic levels (words, Breathe Group) solely based on the assumption that phoneme durations are also Lognormal. As we will see, we find an extremely good quantitative agreement between the NICA and the experimental results of the duration distribution for the case of phonemes and words, but such agreement is less spectacular in the case of Breath Groups. Finally, we discuss our results and justify our recommendation to work with medians instead of with mean values (that assumes Gaussian distribution) to avoid biases and erroneous conclusions in statistical learning studies based on acoustic elements with long-tailed distributions., Postprint (published version)

Multiple approaches to grab comparable data from the Web have been developed up to date. Nevertheless, coming out with a high-quality comparable corpus of a specific topic is not straightforward. We present a model for the automatic extraction of comparable texts in multiple languages and on specific topics from Wikipedia. In order to prove the value of the model, we automatically extract parallel sentences from the comparable collections and use them to train statistical machine translation engines for specific domains. Our experiments on the English–Spanish pair in the domains of Computer Science, Science, and Sports show that our in-domain translator performs significantly better than a generic one when translating in-domain Wikipedia articles. Moreover, we show that these corpora can help when translating out-of-domain texts, Peer Reviewed, Postprint (published version)

In this paper we introduce a joint arc-factored model for syntactic and semantic dependency parsing. The semantic role labeler predicts the full syntactic paths that connect predicates with their arguments. This process is framed as a linear assignment task, which allows to control some well-formedness constraints. For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree. Finally, we employ dual decomposition techniques to produce consistent syntactic and predicate-argument structures while searching over a large space of syntactic configurations. In experiments on the CoNLL-2009 English benchmark we observe very competitive results., Peer Reviewed, Postprint (published version)