Enhanced Spoken Term Detection Using Support Vector Machines and Weighted Pseudo Examples

Spoken term detection (STD) is a key technology for retrieval of spoken content, which will be very important to retrieve and browse multimedia content over the Internet. The discriminative capability of machine learning methods has recently been used to facilitate STD. This paper presents a new approach to improve STD using support vector machines (SVM) based on acoustic information. The concept of pseudo-relevance feedback (PRF) well used in the retrieval of text, image and video is used here. The basic idea of using PRF here is to assume some spoken segments in the first-pass retrieved results are relevant (or pseudo-relevant) and some others irrelevant (or pseudo-irrelevant), and take these segments as positive and negative examples to train a query-specific SVM. This SVM is then used for re-ranking the first-pass retrieved results, and only the re-ranked results are shown to the user. In this paper, feature vectors representing the spoken segments based on acoustic information to be used in SVM are considered and analyzed. Furthermore, conventionally in PRF the items with the highest and lowest scores in the first-pass retrieved results are respectively taken as pseudo-relevant and -irrelevant, but in this way some incorrect examples are inevitably included in the training data especially when the recognition accuracy is poor. Here we further propose an enhanced SVM which not only better selects positive/negative examples considering the reliability of the spoken segments, but emphasizes more on more reliable training examples by modifying the SVM formulation. Experiments on two different sets of spoken archives with different speaking styles and different levels of recognition accuracies demonstrated significant improvements offered by the proposed approaches.