This paper describes a method used to determine if a specific word is related to a certain spoken dialog task. In most ordinary spoken dialog systems, only the words that are actually used to achieve the task are included in the vocabulary. Therefore, the system cannot recognize utterances that contain OOV words that are related to the task. Therefore, we developed a method for determining the words that are related to a specified task in order to augment the system's vocabulary. Our method is based on word similarity. We examined three similarities: word occurrence frequency on the Web, distance in a thesaurus and word similarity using LSA. The experiment revealed that the thesaurus-based and LSA-based methods have an OOV problem. To solve the problem, we developed a way to combine these two methods with the Web-based method. In addition, we tried combining the methods using the AdaBoost algorithm.
|Number of pages||4|
|Journal||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|Publication status||Published - 2008|
|Event||INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia|
Duration: 2008 Sept 22 → 2008 Sept 26
- Web search engine
- Word similarity