Computer Assisted Transcription of Speech
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Inference of finite-state transducers from regular languages
Pattern Recognition
Multi-modal computer assisted speech transcription
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
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Current speech technology allows us to build efficient speech recognition systems. However, model learning of knowledge sources in a speech recognition system is not a closed problem. In addition, lower demand of computational requirements are crucial to building real-time systems. ATROS is an automatic speech recognition system whose acoustic, lexical, and syntactical models can be learnt automatically from training data by using similar techniques. In this paper, an improved version of ATROS which can deal with large smoothed language models and with large vocabularies is presented. This version supports acoustic and syntactical models trained with advanced grammatical inference techniques. It also incorporates new data structures and improved search algorithms to reduce the computational requirements for decoding. The system has been tested on a Spanish task of queries to a geographical database (with a vocabulary of 1,208 words).