... This article motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modeling and considers in detail the Toronto virtual enterprise effort at the University of Toronto.

This paper addresses the issue of Out-Of-Vocabulary (OOV) word detection in Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We propose a method inspired by confidence measures, that consists in analyzing the recognition system outputs in order to automatically detect errors due to OOV words. This method combines various features based on acoustic, linguistic, decoding graph and semantics. We evaluate separately each feature and we estimate their complementarity. Experiments are conducted on a large French broadcast news corpus from the ESTER evaluation campaign. Results show good performance in real conditions: the method obtains an OOV word detection rate of 43%-90 % with 2.5%-17.5 % of false detection. Index Terms: OOV word detection, confidence measures, speech recognition

Abstract—We present a framework for learning a pronunciation lexicon for an Automatic Speech Recognition (ASR) system from multiple utterances of the same training words, where the lexical identities of the words are unknown. Instead of only trying to learn pronunciations for known words we go one step further and try to learn both spelling and pronunciation in a joint optimization. Decoding based on linguistically motivated hybrid subword units generates the joint lexical search space, which is reduced to the most appropriate lexical entries based on a set of simple pruning techniques. A cascade of letter and acoustic pruning, followed by re-scoring N-best hypotheses with discriminative decoder statistics resulted optimal lexical entries in terms of both spelling and pronunciation. Evaluating the framework on English isolated word recognition, we achieve reductions of 7.7 % absolute on word error rate and 14.4 % absolute on character error rate. I.