Data driven search organization for continuous speech recognition

摘要

The authors describe an architecture and search organization for continuous speech recognition. The recognition module is part of the Siemens-Philips-Ipo project on continuous speech recognition and understanding (SPICOS) system for the understanding of database queries spoken in natural language. The goal of this project is a man-machine dialogue system that is able to understand fluently spoken German sentences and thus to provide voice access to a database. The recognition strategy is based on Bayes decision rule and attempts to find the best interpretation of the input speech data in terms of knowledge sources such as a language model, pronunciation lexicon, and inventory of subword units. The implementation of the search has been tested on a continuous speech database comprising up to 4000 words for each of several speakers. The efficiency and robustness of the search organization have been checked and evaluated along many dimensions, such as different speakers, phoneme models, and language models.