Abstract
This paper describes a spoken document retrieval system for processing Malay spoken broadcast news that uses an approach to enhance retrieval performance. An automatic speech recognition (ASR) system was adapted to reduce the impact of ASR transcription errors on retrieval performance. The performance of unsupervised learning was evaluated using Malay broadcast news as the data source. A latent semantic analysis was used to reduce the impact of synonymous words and to identify the story boundaries within the news segments. Among other things, the current system proved to be a powerful instrument to identify news story boundaries automatically.
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