An Active Learning Approach to Task Adaptation

An active learning approach is proposed to automatically analyze speech recognition tasks and select particularly useful adaptation data. In this approach, the distribution of task data is first estimated, which is a combination of two distributions based on N-best recognition results and low confidence data. After that, a subset of adaptation data is selected in two stages using a greedy algorithm according to the estimated distribution. Low confidence data are firstly selected and manually labeled. Then, the high confidence data are selected based on the top-best recognition results, which are also used as labels for the adaptation. The experimental results of the subsequent task adaptation show that the proposed active learning approach can effectively select the useful data to improve the overall performance of the system. The word accuracy is close to, and even exceed, the performance of supervised adaptation using all of the data, when only 10%-20% of the total data need to be manually labeled.