Query-by-Example Spoken Term Detection (QbE-STD) tasks are usually addressed by representing speech signals as a sequence of feature vectors by means of a parametrization step, and then using a pattern matching technique to find the candidate detections. In this paper, we propose a phoneme-based approach in which the acoustic frames are first converted into vectors representing the a posteriori probabilities for every phoneme. This strategy is specially useful when the language of the task is a priori known. Then, we show how this representation can be used for QbE-STD using both a Segmental Dynamic Time Warping algorithm and a graph-based method. The proposed approach has been evaluated with a QbE-STD task in Spanish, and the results show that it can be an adequate strategy for tackling this kind of problems. © Springer-Verlag 2013.
CITATION STYLE
Hurtado, L. F., Calvo, M., Gómez, J. A., García, F., & Sanchis, E. (2013). A phonetic-based approach to query-by-example spoken term detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8258 LNCS, pp. 504–511). https://doi.org/10.1007/978-3-642-41822-8_63
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