TOWARDS UNSUPERVISED SEMANTIC RETRIEVAL OF SPOKEN CONTENT WITH QUERY EXPANSION BASED ON AUTOMATICALLY DISCOVERED ACOUSTIC PATTERNS

This paper presents an initial effort to retrieve semantically related spoken content in a completely unsupervised way. Unsupervised approaches of spoken content retrieval is attractive because the need for annotated data reasonably matched to the spoken content for training acoustic and language models can be bypassed. However, almost all such unsupervised approaches focus on spoken term detection, or returning the spoken segments containing the query, using either template matching techniques such as dynamic time warping (DTW) or model-based approaches. However, users usually prefer to retrieve all objects semantically related to the query, but not necessarily including the query terms. This paper proposes a different approach. We transcribe the spoken segments in the archive to be retrieved through into sequences of acoustic patterns automatically discovered in an unsupervised method. For an input query in spoken form, the top-N spoken segments from the archive obtained with the first-pass retrieval with DTW are taken as pseudo-relevant. The acoustic patterns frequently occurring in these segments are therefore considered as query-related and used for query expansion. Preliminary experiments performed on Mandarin broadcast news offered very encouraging results.