Efficient speech indexing and search for embedded devices using uniterms

In this paper, we present an efficient method of speech indexing and search using phoneme sequences called uniterms. In the indexing stage, a collection of uniterms and uniterm sequences is extracted from the target speech database by applying statistical scoring to each data item´s phoneme lattice. In the search stage, each speech query´s phoneme lattice is used to select candidate uniterms from the collection. These uniterms are applied in a speech recognition engine to convert the speech query into a uniterm lattice, from which we obtain a set of candidate uniterm sequences, each of which can be mapped to a search result item. Not only is this method a significant improvement over previous phoneme-based methods, it is shown that explicit sequential comparison of uniterms in query and target data can be avoided using the proposed method without loss of search performance. Avoiding sequential comparison allows better handling of transposition of words, and for the case where queries have word orders different from their intended targets, the proposed method can potentially bring about significant improvement.