An efficient voice retrieval system for very-large-vocabulary Chinese textual databases with a clustered language model
Sung‐Chien Lin, Lee‐Feng Chien, Keh-Jiann Chen, Lin-shan Lee · 2002
This paper presents an accurate and efficient voice retrieval system for very-large-vocabulary Chinese textual databases with a specially-designed clustered language model. To reduce the problems resulted from the complexity of unconstrained speech-input queries for retrieval, the system is completely syllable-based in both speech recognition and database retrieval by properly utilizing the mono-syllabic structure of Chinese language. In addition, it partitions the records in the database into clusters and trains the clustered language model using the clustering results. The proposed clustered language model with its augmented search algorithm are very useful to improve accuracy and speed of the speech retrieval system. In the preliminary tests using an experimental database with about 30,000 bibliographical records, it was found that the present system can accept unconstrained speech-input queries and achieve very good performance.