PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

To recover from speech recognition errors in spoken document retrieval
Mikko Kurimo and Ville Turunen
In: Eurospeech 2005, 5-8 Sep 2005, Lisbon, Portugal.

Abstract

An important difference between the retrieval of spoken and written documents is that the indexing of the speech data is usually based on automatic speech transcripts that contain recognition errors. However, there are several ways of reducing the effect of incorrect index terms in the retrieval. This paper presents retrieval experiments with unlimited vocabulary speech recognizer that utilizes a lexicon of unsupervised morpheme-like units. Based on this recognizer, three different methods are evaluated for error recovery. First, the recognized words are expanded by adding the recognized morphemes, too. Second, the words are expanded by adding the best rival morpheme candidates that were pruned away by the recognizer. Third, the queries are expanded by the potentially relevant terms found from text documents, which were retrieved from parallel text corpora by the original queries. The best results are obtained by that latter method which significantly improves the precision compared to the original queries and brings the spoken document retrieval precision to the same level as the corresponding text document retrieval.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Natural Language Processing
Speech
Information Retrieval & Textual Information Access
ID Code:1035
Deposited By:Mikko Kurimo
Deposited On:07 August 2005