An integration method of multiple search results for spoken term detection

We propose a new integration method of multiple search results for improving search accuracy of Spoken Term Detection (STD). A usual STD system prepares two types of recognition results of spoken documents. If a query consists of in-vocabulary (IV) terms, the results using word-based recognizer are used, and if a query includes out-of-vocabulary (OOV) terms, the results using subword-based recognizer are used. The paper proposes an integration method of these two search results. Each utterance has a similarity score included in the search results. The scores of two results for an utterance has been integrated linearly using a constant weighting factor so far. Our preliminary experiments showed the search accuracy using the subword-based results was higher for some IV queries. In the same way, that using the word-based results was higher for some OOV queries. In the proposed method, the similarity scores of the two search results are compared for the same utterance and a higher weighing factor is given to the results that showed a higher similarity score. The proposed method is evaluated using open test sets, and experimental results demonstrated the search accuracy improved for all test sets.