A new approach to adaptive Semantic-Language modelling has recently been proposed which allows automatic learning of all the acoustic and syntactic-semantic models that are required for a given Continuous Speech Recognition task. The proposed approach is based on the so called "Error Correcting Grammatical Inference" algorithm which supplies homogeneous finite-state structural models both at the acoustic and at the syntactic-semantic levels. Recognition or Understanding is seen as a Formal Transduction procedure that exploits the set of acoustic and linguistic constraints that have been captured in the learned models to directly input raw acoustic signals and output the semantic messages that are conveyed by these signals. In this paper the proposed approach is reviewed and new improvements are presented. Also, preliminary results with a large semantic-space continuous speech task (Spanish numbers in the one-million range) are presented showing the currently achieved capabilities of this approach. KEYWORDS: Language Modelling, Grammatical Inference, Adaptive Language Acquisition, Speech Recognition.
Bibliographic reference. Prieto, Natividad / Vidal, Enrique (1991): "Learning language models through the ECGI method", In EUROSPEECH-1991, 395-398.