We aim to improve a speech understanding module with a small amount of training data. A speech understanding module uses a language model (LM) and a language understanding model (LUM). A lot of training data are needed to improve the models. Such data collection is, however, difficult in an actual process of development. We therefore design and develop a new framework that uses multiple LMs and LUMs to improve speech understanding accuracy under various amounts of training data. Even if the amount of available training data is small, each LM and each LUM can deal well with different types of utterances and more utterances are understood by using multiple LM and LUM. As one implementation of the framework, we develop a method for selecting the most appropriate speech understanding result from several candidates. The selection is based on probabilities of correctness calculated by logistic regressions. We evaluate our framework with various amounts of training data.
|ジャーナル||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|出版ステータス||Published - 2009|
|イベント||10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom|
継続期間: 2009 9月 6 → 2009 9月 10
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