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In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states – confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of ‘most important ’ features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics

Topic adaptation for lecture translation through bilingual latent semantic models”.

Abstract This work presents a simplified approach to bilingual topic modeling for language model adaptation by combining text in the source and target language into very short documents and performing Probabilistic Latent Semantic Analysis (PLSA) during model training. During inference, documents containing only the source language can be used to infer a full topic-word distribution on all words in the target language's vocabulary, from which we perform Minimum Discrimination Information (MDI) adaptation on a background language model (LM). We apply our approach on the English-French IWSLT 2010 TED Talk exercise, and report a 15% reduction in perplexity and relative BLEU and NIST improvements of 3% and 2.4%, respectively over a baseline only using a 5-gram background LM over the entire translation task. Our topic modeling approach is simpler to construct than its counterparts

1 Classification Methods for Structured Outputs

Abstract- This paper is conceived as a summary of previous works that explored many alternative learning models for classification of documents on structured outputs. We provide a discussion of strong and weak points for each method. I

Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models

Abstract Learning from errors is a crucial aspect of improving expertise. Based on this notion, we discuss a robust statistical framework for analysing the impact of different error types on machine translation (MT) output quality. Our approach is based on linear mixed-effects models, which allow the analysis of error-annotated MT output taking into account the variability inherent to the specific experimental setting from which the empirical observations are drawn. Our experiments are carried out on different language pairs involving Chinese, Arabic and Russian as target languages. Interesting findings are reported, concerning the impact of different error types both at the level of human perception of quality and with respect to performance results measured with automatic metrics