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This paper gives an overview of a research system for phoneme-based large-vocabulary continuous-speech recognition. The system provides the speaker-dependent recognition component in the speech-understanding system SPICOS that is designed to recognize and understand database queries spoken in natural German language. The recognition technique used in the SPICOS recognition system is based on an integrated approach that combines the various knowledge sources, such as an inventory of subword units, pronunciation lexicon and language model, during the process of decision making in order to improve the reliability of the acoustic recognition. The recognition problem then amounts to an efficient search for the globally optimal decision through a huge state space. The size of this state space depends primarily on the type of language model being used. Stochastic bigram and trigram models are studied and compared with the case of no language model restrictions. Recognition experiments have been carried out on a 917-word task for 4 speakers. For each speaker, 200 sentences totalling 1391 words had to be recognized.

Phoneme-Based Continuous-Speech Recognition in the SPICOS-II System: Phonembasierte Erkennung kontinuierlicher Sprache im Projekt SPICOS-II