Sibyl, a factoid question-answering system for spoken documents
ACM Transactions on Information Systems (TOIS)
Future trends in business analytics and optimization
Intelligent Data Analysis
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The output of a speech recognition system is a stream of text features that is overlayed by noise resulting from errors in the system's statistical classification of the audio input. Conditional Random Fields (CRFs), which have already proven themselves to be efficient, high-performance Named Entity Recognizers (NERs) for named entities from text, offer the promise to compensate part of these errors. In this paper we use CRFs to extract named entities from spoken audio documents. We consider a real-world audio information extraction scenario under which CRFs are trained to recognize named entities in unedited radio audio documents that have been converted into a stream of text features by a speech recognition system. The automatic speech recognition system (ASR) is able to produce word transcriptions as well as syllables. It uses general speaker-independent acoustic models and a domain-independent statistical language model, insuring that recognizer performance is not specific to the experimental domain. Using an additional syllable model increases the generality of the spoken document classification system, giving it the flexibility to handle words that are not present in the vocabulary. In this paper we apply for the first time CRFs to different features produced by German ASR. The experiments confirm that using transcribed syllables together with words can compensate for part of the NER errors caused by ASR transcription.