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Creation of an Annotated German Broadcast Speech Database for Spoken Document Retrieval

In this paper we present a semi-automatic method for creating annotated data sets from German-language broadcast resources for which audio files as well as transcripts are available on the Internet. The transcripts are required to be reasonably accurate, but not perfect. Our approach is implemented by a integrated bundle of data processing tools, which support the human annotator in the creation of an annotated data set specialized for research in the area of spoken document classification and retrieval. Annotation decisions that would require prohibitively large amounts training data or system development time to make automatically are taken over by the human annotator. Annotation decisions which are easily automated and tedious for humans are shouldered by the computer. Using our method we can process and annotate the data approximately ten times faster that it was possible by hand. The data is downloaded and the transcripts are normalized by a series of filters as well as a semi-automatic digit to text conversion. Then, the system makes use of the Bayesian Information Criterion (BIC) to segment the audio data and Automatic Speech Recognition (ASR) to forced-alignment of the speech signal with written transcripts. We demonstrate the method with the concrete example of our Deutsche Welle database of programs from the Kalenderblatt radio series.