Abstract
In the current study, the BASRAH system was used to calculate confidence measures (CMs) and then use them to designate individual words provided by an automatic speech recognition system (ASR) as either accept or reject. This information about a recognized word can be used to reduce the impact of ASR transcription errors on retrieval performance. The system also can process multilingual broadcasts, which is more challenging than dealing with a single language. The BASRAH system is able to provide CMs for ASR output for large data sets based on a word acoustic score. In a case study, we successfully used the BASRAH system to first calculate CMs to clean up spoken multilingual (English and Malay) broadcast news transcription and then to identify the boundaries of the broadcast news stories.
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Khalaf Aleqili, Z.A. (2012). The BASRAH System: A Method for Spoken Broadcast News Story Clustering. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30507-8_12
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DOI: https://doi.org/10.1007/978-3-642-30507-8_12
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