Abstract
This paper reports on the evaluation of a system that allows the use of spoken queries to retrieve information from a textual document collection. First, a large vocabulary continuous speech recognizer transcribes the spoken query into text. Then, an information retrieval engine retrieves the documents relevant to that query. The system works for Spanish language. In order to increase performance, we proposed a two-pass approach based on dynamic adaptation of language models. The system was evaluated using a standard IR test suite from CLEF. Spoken queries were recorded by 10 different speakers. Results showed that the proposed approach outperforms the baseline system: a relative gain in retrieval precision of 5.74%, with a language model of 60,000 words.
This work has been partially supported by Consejería de Educació n de la Junta de Castilla y León under project number VA053A05.
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González-Ferreras, C., Cardeñoso-Payo, V. (2007). Dynamic Adaptation of Language Models in Speech Driven Information Retrieval. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_29
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DOI: https://doi.org/10.1007/978-3-540-74628-7_29
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