A one-pass decoder based on polymorphic linguistic context assignment

In this study, we examine how fast decoding of conversational speech with large vocabularies profits from efficient use of linguistic information, i.e. language models and grammars. Based on a re-entrant single pronunciation prefix tree, we use the concept of linguistic context polymorphism to allow...

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Bibliographic Details
Main Authors: Soltau, H., Metze, F., Fugen, C., Waibel, A.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic beams
Automatic speech recognition
Context modeling
Decoding
History
Interactive systems
Laboratories
Natural languages
Speech recognition
Vocabulary
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Summary:In this study, we examine how fast decoding of conversational speech with large vocabularies profits from efficient use of linguistic information, i.e. language models and grammars. Based on a re-entrant single pronunciation prefix tree, we use the concept of linguistic context polymorphism to allow an early incorporation of language model information. This approach allows us to use all available language model information in a one-pass decoder, using the same engine to decode with statistical n-gram language models as well as context free grammars or re-scoring of lattices in an efficient way. We compare this approach to our previous decoder, which needed three passes to incorporate all available information. The results on a very large vocabulary task show that the search can be speeded up by almost a factor of three, without introducing additional search errors.
DOI:10.1109/ASRU.2001.1034625