Development of lexical resources is, along with grammar development, one of the main efforts when building multilingual NLP applications. In this paper, we present a tool-based approach for more efficient manual lexicon development for a spoken language translation system. The approach in particular addresses the common problems of multilingual lexica including the redundancy of encoded information and inconsistency of lexica of different languages. The general benefits of this practical tool-based approach are clear and user-friendly lexicon structure, inheritance of information inside of a language and between different system languages, and transparency and consistency of coverage between system languages. The visual tool-based approach is user-friendly to linguistic informants that don’t have previous experience of lexicon development, while at the same time, it still is a powerful tool for expert system developers.

Aujourd’hui, l’approche la plus courante en traitement de la parole consiste à combiner un reconnaisseur statistique avec un analyseur robuste. Pour beaucoup d’applications cependant, les reconnaisseurs linguistiques basés sur les grammaires offrent de nombreux avantages. Dans cet article, nous présentons une méthodologie et un ensemble de logiciels libres (appelé Regulus) pour dériver rapidement des reconnaisseurs linguistiquement motivés à partir d’une grammaire générale partagée pour le catalan et le français.

We present an overview of Regulus, an Open Source platform that supports corpus-based derivation of efficient domain-specific speech recognisers from general linguistically motivated unification grammars. We list available Open Source resources, which include compilers, resource grammars for various languages, documentation and a development environment. The greater part of the paper presents a series of experiments carried out using a medium-vocabulary medical speech translation application and a corpus of 801 recorded domain utterances, designed to investigate the impact on speech understanding performance of vocabulary size, grammatical coverage, presence or absence of various linguistic features, degree of generality of thegrammar and use or otherwise of probabilistic weighting in the CFGlanguage model. In terms of task accuracy, the most significant factors were the use of probabilistic weighting, the degree of generality of the grammar and the inclusion of features which model sortal restrictions.

In this paper, we present evidence that providing users of a speech to speech translation system for emergency diagnosis (MedSLT) with a tool that helps them to learn the coverage greatly improves their success in using the system. In MedSLT, the system uses a grammar-based recogniser that provides more predictable results to the translation component. The help module aims at addressing the lack of robustness inherent in this type of approach. It takes as input the result of a robust statistical recogniser that performs better for out-of-coverage data and produces a list of in-coverage example sentences. These examples are selected from a defined list using a heuristic that prioritises sentences maximising the number of N-grams shared with those extracted from the recognition result.