Speech Retrieval Engine Based on Relevance Feedback
Shao Qian-ming · Jisuanji gongcheng · 2007
This paper approaches and implements a retrieval engine for speech data based on relevance feedback,imposing Sphinx system to transfer audio file to text file then indexed by Lucene system.To improve the quality of the query answering,the paper employs an overall relevance feedback,including local feedback and global feedback.It improves the system's accuracy and dynamic characteristic.Experimental results prove the engine meets the users'demands and it's of practice value.