Using acoustic dissimilarity measures based on state-level distance vector representation for improved spoken term detection

This paper proposes a simple approach to subword-based spoken term detection (STD) which uses improved acoustic dissimilarity measures based on a distance-vector representation at the state-level. Our approach assumes that both the query term and spoken documents are represented by subword units and then converted to the sequence of HMM states. A set of all distributions in subword-based HMMs is used for generating distance-vector representation of each state of all subword units. The element of a distance-vector corresponds to the distance between distributions of two different states, and thus a vector represents a structural feature at the state-level. The experimental result showed that the proposed method significantly outperforms the baseline method, which employs a conventional acoustic dissimilarity measure based on subword unit, with very little increase in the required search time.