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Towards Speech-Driven Question Answering: Experiments Using the NTCIR-3 Question Answering Collection

We developed a method for producing statistical language models for speech-driven question answering, which recognizes spoken questions with high accuracy. Our method uses a target collection (i.e., a document set from which answers are derived) to extract N-grams, and adapts them to the questionanswering task by way of frozen patterns typically used in interrogative questions. In addition, our method magnifies N-gram statistics corresponding to frozen patterns in the original N-gram. For the purpose of experiments, we used dictated questions in the NTCIR-3 QAC test collection, and showed that our method outperformed a conventional language model adaptation method in terms of the speech recognition accuracy.