Publications

Document summarization algorithms are most commonly evaluated according to the intrinsic quality of the summaries they produce. An alternate approach is to examine the extrinsic utility of a summary, measured by the ability of the summary to aid a human in the completion of a specific task. In this paper...

This paper explores both supervised and unsupervised topic modeling for spoken audio documents using only phonetic information. In cases where word-based recognition is unavailable or infeasible, phonetic information can be used to indirectly learn and capture information provided by topically relevant lexical items. In some situations, a lack of transcribed...

In this paper, we discuss the use of minimum classification error (MCE) training as a means for improving traditional approaches to topic identification such as naive Bayes classifiers and support vector machines. A key element of our new MCE training techniques is their ability to efficiently apply jackknifing or leave-one-out...

This work presents techniques for automatically summarizing the topical content of an audio corpus. Probabilistic latent semantic analysis (PLSA) is used to learn a set of latent topics in an unsupervised fashion. These latent topics are ranked by their relative importance in the corpus and a summary of each topic...

In this chapter we discuss the problem of identifying the underlying topics beings discussed in spoken audio recordings. We focus primarily on the issues related to supervised topic classification or detection tasks using labeled training data, but we also discuss approaches for other related tasks including novel topic detection and...

Document similarity measures are required for a variety of data organization and retrieval tasks including document clustering, document link detection, and query-by-example document retrieval. In this paper we examine existing and novel document similarity measures for use with spoken document collections processed with automatic speech recognition (ASR) technology. We compare...

This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over...

This paper examines a query-by-example approach to spoken term detection in audio files. The approach is designed for low-resource situations in which limited or no in-domain training material is available and accurate word-based speech recognition capability is unavailable. Instead of using word or phone strings as search terms, the user...

In this paper we examine an alternative interface for phonetic search, namely query-by-example, that avoids OOV issues associated with both standard word-based and phonetic search methods. We develop three methods that compare query lattices derived from example audio against a standard ngrambased phonetic index and we analyze factors affecting the...

The success of support vector machines (SVMs) for classification problems is often dependent on an appropriate normalization of the input feature space. This is particularly true in topic identification, where the relative contribution of the common but uninformative function words can overpower the contribution of the rare but informative content...