A spoken-access approach for chinese text and speech information retrieval

This paper presents an efficient spoken-access approach for both Chinese text and Mandarin speech information retrieval. The proposed approach is developed not only to deal with the retrieval of spoken documents, but also to improve the capability of human-computer interaction via voice input for information-retrieval systems. Based on utilization of the monosyllabic structure of the Chinese language, the proposed approach can tolerate speech recognition errors by performing speech query recognition and approximate information retrieval at the syllable-level. Furthermore, with the help of automatic term suggestion and relevance feedback techniques, the proposed approach is robust in enabling users using voice input to interact with IR systems at each stage of the retrieval process. Extensive experiments show that the proposed approach can improve the effectiveness of information retrieval via speech interaction. The encouraging results suggest that a Mandarin speech interface for information retrieval and digital library systems can, therefore, be developed.