Evaluation of re-ranking by prioritizing highly ranked documents in spoken term detection
We have been proposing a Spoken Term Detection (STD) method for Out-Of-Vocabulary (OOV) query terms using various subword units, such as monophone, triphone, demiphone, one third phone, and Sub-phonetic segment (SPS) models. In the proposed method, subword-based ASR is performed for all spoken documents and subword recognition results are generated using subword acoustic models and subword language models. When a query term is given, the subword sequence of the query term is searched for all subword sequences of subword recognition results of spoken documents. Here, we use acoustical distances between subwords when matching the two subword sequences in Continuous Dynamic Programming. Demiphone and one-third phone models were newly developed for an STD task. We have also proposed the method integrating plural STD results obtained using each subword models. Each candidate segment has a distance, the segment number and the document number. These plural distances are integrated linearly using weighting factors. In STD tasks of IR for Spoken Documents in NTCIR-9, we apply various subword models to the STD tasks and integrate plural STD results obtained from these subword models.