In this paper the performance of different confidence measures used for an address recognition system are evaluated. The recognition system for cursive handwritten German address words is based on Hidden Markov Models (HMMs). It is essential, that the structure of the address (name, street, city, country) is known, so that a specific small but complete dictionary can be selected. Choosing a wrong dictionary (OOV: out-of-vocabulary) or misrecognize the word, the recognition result should be rejected by means of the confidence measure. This paper points out two aspects: the comparison of four confidence measures for single words -- based on the likelihood, a garbage-model, a two-best recognition or a character decoding -- and the comparison of using complete or wrong dictionaries. It is shown, that the best confidence measure -- the two-best distance -- has a quite different behavior using OOV.

This paper adresses the correct choice and combination of confidence measures in large vocabulary speech recognition tasks. We classify single words within continuous as well as large vocabulary utterances into two categories: utterances within the vocabulary which are recognized correctly, and other utterances, namely misrecognized utterances or (less frequent) out-of-vocabulary (OOV).

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. 1.