Effective utilization of multiple examples in query-by-example spoken term detection
This paper investigates the example utilization problem in query-by-example spoken term detection when multiple examples are provided for each query term. To achieve this goal, we propose three evaluation metrics to assess the quality of all the examples, namely posteriorgram stability score, pronunciation reliability score and local similarity score. We also present a clustering based example generation approach to creating better examples based on the original ones. Experiments conducted on a telephone speech corpus shows that it is better to use several representative examples selected by the quality assessment process than to simply use all the examples. Furthermore, even better results can be obtained if the generated examples are used.