Spoken query processing for interactive information retrieval

It has long been recognised that interactivity improves the effectiveness of information retrieval systems. Speech is the most natural and interactive medium of communication and recent progress in speech recognition is making it possible to build systems that interact with the user via speech. However, given the typical length of queries submitted to information retrieval systems, it is easy to imagine that the effects of word recognition errors in spoken queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical information retrieval techniques for spoken query processing is robust to considerably high levels of word recognition errors, in particular for long queries. Moreover, in the case of short queries, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of spoken query processing

Retrieval effectiveness of written and spoken queries : an experimental evaluation

With the fast growing speech technologies, the world is emerging to a new speech era. Speech recognition has now become a practical technology for real world applications. While some work has been done to facilitate retrieving information in speech format using textual queries, the characteristics of speech as a way to express an information need has not been extensively studied. If one compares written versus spoken queries, it is intuitive to think that users would issue longer spoken queries than written ones, due to the ease of speech. Is this in fact the case in reality? Also, if this is the case, would longer spoken queries be more effective in helping retrieving relevant document than written ones? This paper presents some new findings derived from an experimental study to test these intuitions

The effect of component recognition on flexibility and speech recognition performance in a spoken question answering system

A spoken question answering system that recognizes questions as full sentences performs well when users ask one of the questions defined. A system that recognizes component words and finds an equivalent defined question might be more flexible, but is likely to have decreased speech recognition performance, leading to a loss in overall system success. The research described in this document compares the advantage in flexibility to the loss in recognition performance when using component recognition. Questions posed by participants were processed by a system of each type. As expected, the component system made frequent recognition errors while detecting words (word error rate of 31%). In comparison, the full system made fewer errors while detecting full sentences (sentence error rate of 10%). Nevertheless, the component system succeeded in providing proper responses to 76% of the queries posed, while the full system responded properly to only 46%. Four variations of the traditional tf-idf weighting method were compared as applied to the matching of short text strings (fewer than 10 words). It was found that the general approach was successful in finding matches, and that all four variations compensated for the loss in speech recognition performance to a similar degree. No significant difference due to the variations in weighting was detected in the results