An approach for spoken term detection based on modified Gaussian posteriorgrams

Query-by-Example Spoken Term Detection(QbE-STD) has been a hot research topic in speech recognition field. While template representation is the key composition part of QbE-STD, many researchers have been committed to developing effective template representations to obtain the better performance. Gaussian posteriorgram has been widely used due to that the GMM model which generates the Gaussian posteriorgram can be convenient and easy to train. However, the corresponding performance is not that satisfactory. In this paper, we use modified Gaussian posteriorgram based on the proposed Gaussian components selection algorithm as template representation, which emphasizes the discriminant among queries. The selection algorithm is inspired by the TF-IDF concept well known to the information retrieval and text indexing fields. We carried out comparison on the TIMIT corpus, and the results showed that, with our approach, the P@N was increased by 12%, and the EER was reduced by 10%.

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