Case based reasoning solution to the problem of sustained learning in keyword spotting

In some practical keyword spotting applications, users or service providers are willing to provide spotting-result feedback to help improve system performance. To do so, they require a keyword spotting technique with a sustained learning ability. This paper presents a new Chinese keyword spotting method based on a case based reasoning framework. Two level keyword case representations are adopted based on a set of symbols that are discriminative both in acoustic feature vector space and in semantic space. Then case bases are indexed with a tree structure and searched for test speech based on an elastic matching strategy. Finally, the feedback is used to adjust the statistics attached to the cases or to append new cases. Two experiments were conducted to compare our approach with a syllable lattice based method and to test the sustained learning ability.