Relevant document retrieval using a spoken document

In this paper, we proposed a method of retrieving documents from the World Wide Web using a spoken document as a "key." This method can be viewed as a speech version of an ordinary relevant document retrieval, where a text document is used as a query of retrieval. Basically the retrieval is based on an automatic transcription of a spoken document using a speech recognizer. The difficult point of this task is that the automatic transcription contains many recognition errors, therefore we cannot trust keywords extracted from the automatic transcription using conventional method such as tf·idf. To solve this problem, we developed three methods. The first one is to measure relevance of a keyword to the spoken document by using Web documents retrieved using a Web search engine by specifying the keyword as a query. The second one is to compose a query from the selected keywords so that words derive from misrecognitions are excluded and similar words are gathered. The third one is to measure relevance of a downloaded Web document to the spoken document. The experimental results suggest that the proposed methods are promising for retrieving relevant documents of a spoken document.