Unsupervised query-by-example spoken term detection using segment-based Bag of Acoustic Words

In this work, we present an unsupervised framework to address the problem of spotting spoken terms in large speech databases. The segment-based Bag of Acoustic Words (BoAW) framework proposed is inspired from the Bag of Words (BoW) approach widely used in text retrieval systems. Since this model ignores the sequence information in speech samples for efficient indexing of the database, a Dynamic Time Warping (DTW) based temporal matching technique is used to re-rank the results and restore the time sequence information. The speech data is stored efficiently in an inverted index which makes the retrieval very fast, thus making this framework particularly useful for searching large databases. We address the issue of choosing the appropriate size of the segment of speech for reliable indexing. Comparison with other query-by-example spoken term detection systems shows that the proposed system outperforms the rest.