A Phonetic-Based Approach to Query-by-Example Spoken Term Detection

Abstract

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.

DOI: 10.1007/978-3-642-41822-8_63

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Cite this paper

@inproceedings{Hurtado2013APA, title={A Phonetic-Based Approach to Query-by-Example Spoken Term Detection}, author={Llu{\'i}s F. Hurtado and Marcos Calvo Lafarga and Jon Ander G{\'o}mez and Fernando Garc{\'i}a and Emilio Sanchis Arnal}, booktitle={CIARP}, year={2013} }