We describe here a collection of speech data of bilingual and trilingual speakers of English, French, German and Italian. In the context of speech to speech translation (S2ST), this database is designed for several purposes and studies: training CLSA systems (cross-language speaker adaptation), conveying emphasis through S2ST systems, and evaluating TTS systems. More precisely, 36 speakers judged as accentless (22 bilingual and 14 trilingual speakers) were recorded for a set of 171 prompts in two or three languages, amounting to a total of 24 hours of speech. These sets of prompts include 100 sentences from news, 25 sentences from Europarl, the same 25 sentences with one acted emphasised word, 20 semantically unpredictable sentences, and finally a 240-word long text. All in all, it yielded 64 bilingual session pairs of the six possible combinations of the four languages. The database is freely available for non-commercial use and scientific research purposes.

This paper focuses on the use of advanced techniques of text analysis as support for collocation extraction. A hybrid system is presented that combines statistical methods and multilingual parsing for detecting accurate collocational information from English, French, Spanish and Italian corpora. The advantage of relying on full parsing over using a traditional window method (which ignores the syntactic information) is first theoretically motivated, then empirically validated by a comparative evaluation experiment.

TwicPen is a terminology-assistance system for readers of printed (ie. off-line) material in foreign languages. It consists of a hand-held scanner and sophisticated parsing and translation software to provide readers a limited number of translations selected on the basis of a linguistic analysis of the whole scanned text fragment (a phrase, part of the sentence, etc.). The use of a morphological and syntactic parser makes it possible (i) to disambiguate to a large extent the word selected by the user (and hence to drastically reduce the noise in the response), and (ii) to handle expressions (compounds, collocations, idioms), often a major source of difficulty for non-native readers. The system exists for the following language-pairs: English-French, French-English, German-French and Italian-French.

Inter-word associations like stagger - drunken, or intra-word sense divisions (e.g. write a diary vs. write an article) are difficult to compile using a traditional lexicographic approach. As an alternative, we present a model that reflects this kind of subtle lexical knowledge. Based on the minimal sense of a word (clique), the model (1) selects contextually related words (contexonyms) and (2) classifies them in a multi-dimensional semantic space. Trained on very large corpora, the model provides relevant, organized contexonyms that reflect the fine-grained connotations and contextual usage of the target word, as well as the distinct senses of homonyms and polysemous words. Further study on the neighbor effect showed that the model can handle the data sparseness problem.

This contribution is a report on an on-going research aiming at the development of a speech recognition system for French, combining a standard HMM recognition tool with a syntactic parser. Because of the very high number of homophones in French and because several agreement rules spread over an unbounded number of words, we designed our GB-based morphological and syntactic parser to output a correct orthographic form from a lattice of phonemes produced by the front-head HMM recognition system. This resulting lattice of phonemes is processed by the syntactic parser, which selects the best word sequence according to its linguistic knowledge. The originality of this approach is the use of a syntactic parser, tuned to phonetic inputs, for a speech recognition task.