Experiments in Spoken Document Retrieval at CMU
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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We describe our submission to the TREC-6 Spoken Document Retrieval SDR track and the speech recognition and the information retrieval engines. We present SDR evaluation results and a brief analysis. A few developments and experiments are also described in detail including Vocabulary size experiments, which assess the effect of words missing from the speech recognition vocabulary. For our 51,000-word vocabulary the effect was minimal. Speech recognition using a stemmed language model, where the model statistics of words containing the same root are combined. Stemmed language models did not improve speech recognition or information retrieval. Merging the IBM and CMU speech recognition data. Combining the results of two independent recognition systems slightly boosted information retrieval results. Confidence annotations that estimate of the correctness of each recognized word. Confidence annotations did not appear to improve retrieval. N-best lists where the top recognizer hypotheses are used for information retrieval. Using the top 50 hypotheses dramatically improved performance in the test set. Effects of corpus size on the SDR task. As more documents are added to the task, the gap between perfect retrieval and retrieving spoken documents gets larger. This makes it clear that the size of the current TREC SDR track corpus is too small for obtaining meaningful results. While we have done preliminary experiments with these approaches, most of them were not part of our submission, since their impact on the IR performance on the actual TREC SDR training corpus was too marginal for reliable experiments.