Speech is the primary and most convenient means of communication between individuals [1]. In the future network era, the digital content over the network will include all the information activities for human life, from real-time information to knowledge archives, from working environments to private services. Apparently, the most attractive form of the network content will be in multimedia, including speech information. Such speech information usually provides insight concerning the subjects, topics, and concepts of the multimedia content. As a result, the spoken documents associated with the network content will become key for retrieval and browsing. On the other hand, the rapid development of network and wireless technologies is making it possible for people to access the network content not only from the office/home, but from anywhere, at any time, via small handheld devices such as personal digital assistants (PDAs) or cell phones. Today, network access is primarily text based. The users enter the instructions by words or texts, and the network or search engine offers text materials from which the user can select. The users interact with the network or search engine and obtain the desired information via text-based media. In the future, it can be imagined that almost all such functions of text can also be performed with speech. The user’s instructions can be entered not only by text but possibly through speech as well since speech is a convenient user interface for a variety of user terminals, especially for small handheld devices. The network content may be indexed/retrieved and browsed not only by text but possibly also by the associated spoken documents as mentioned above. The users may also interact with the network or the search engine via either text-based media or spoken/multimodal dialogs. Text-to-speech synthesis can be used to transform the text information in the content into speech when required. This is the general environment of retrieval/browsing applications for multimedia content with associated spoken documents.

This paper presents an indexing system for spoken audio documents. The framework is indexing and retrieval of broadcast news. The proposed indexing system applies latent semantic analysis (LSA) and self-organizing maps (SOM) to map the documents into a semantic vector space and to display the semantic structures of the document collection. The SOM is also used to enhance the indexing of the documents that are dicult to decode. Relevant index terms and suitable index weights are computed by smoothing the document vectors with other documents which are close to it in the semantic space. Experimental results are provided using the test data of the TREC's spoken document retrieval track.