Improved Chinese spoken document retrieval with hybrid modeling and data-driven indexing features

Different models retrieve the documents based on different approaches of extracting the underlying content. Different levels of indexing features also offer different functionalities and discriminabilities when retrieving the documents. In this paper, we present results for Chinese spoken document retrieval with hybrid models to integrate the knowledge obtainable from three basic retrieval models, namely, the standard vector space model (VSM), the hidden Markov model (HMM), and the latent semantic indexing (LSI) model. The characteristics of retrieval performance using both word-level and syllable-level indexing features were extensively explored. In addition, a data-driven approach to derive variable-length indexing features is also presented. Very satisfactory performance can be achieved with these data-driven features while retaining very compact feature set size. Experiments showed that this approach has the potential to identify domain-specific terminologies or newlygenerated phrases. It is therefore very useful not only in Chinese document retrieval, but also in detecting out of vocabulary (OOV) words in Chinese. Very encouraging results were obtained when the hybrid models were used with the datadriven indexing features as well.