Intensive acoustic models constructed by integrating low-occurrence models for spoken term detection

Triphone acoustic models are often used as subword models for detecting out-of-vocabulary query terms in Spoken Term Detection (STD) systems. Our preliminary experiments revealed that the training data for a large portion of the approximately 8,000 triphone models are insufficient. Assuming that such insufficient models deteriorate the performance of STD, this paper proposes intensive triphone models constructed by integrating low-occurrence triphone models into high-occurrence ones. Experiments conducted using an actual lecture speech corpus showed that the proposed method improves the STD performance with regard to both triphones and demiphones, demonstrating its effectiveness.