In this paper we report on progress made at LIMSI in speaker-independent large vocabulary speech dictation using newspaper-based speech corpora in English and French. The recognizer makes use of continuous density HMMs with Gaussian mixtures for acoustic modeling and n-gram statistics estimated on newspaper texts for language modeling. Acoustic modeling uses cepstrum-based features, context-dependent phone models (intra and interword), phone duration models, and sex-dependent models. For English the ARPA Wall Street Journal-based CSR corpus is used and for French the BREF corpus containing recordings of texts from the French newspaper Le Monde is used. Experiments were carried out with both these corpora at the phone level and at the word level with vocabularies containing up to 20,000 words. Word recognition experiments are also described for the ARPA RM task which has been widely used to evaluate and compare systems.

The aim of the LRE-SQALE project (Speech recognizer Quality Assessment for Linguistic Engineering) is to experiment with establishing an evaluation paradigm in Europe for the assessment of large-vocabulary, continuous speech recognition systems in a multilingual environment. This 18 month project is will define and carry out the assessment experiments, paving the way for future projects with a larger scope and wider participation of European sites. The SQALE Consortium consists of a coordinator, the Institute for Human Factors at TNO, and three laboratories (CUED, LIMSI-CNRS, PHILIPS) who will evaluate their recognition systems using commonly agreed upon protocols, with the evaluation organized by the coordinating laboratory. Multiple sites will test their algorithms on the same database, so as to compare the merits of different methods, and each site will evaluate on at least two languages, so as to compare the relative difficulties of the languages, and the degree of independency of ...

In this paper we report efforts at LIMSI in speaker independent large vocabulary speech recognition in French and in English. The recognizer makes use of continuous density HMM (CDHMM) with Gaussian mixture for acoustic modeling and n-gram statistics estimated on text material for language modeling. Acoustic modeling uses cepstrum-based features, context-dependent phone models (intra and interword), phone duration models, and sex-dependent models. The