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Content-based recommender systems use preference ratings and features that characterize media to model users' interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies...
We aim to improve term detection performance by augmenting traditional N-gram language models with multiple levels of topic context. We demonstrate that incorporating complementary aspects of topicality leads to significant improvements in term detection accuracy. We represent broad topic context through document-specific latent topics inferred via a Bayesian topic model. We capture local topic context...
We consider the task of identifying topics in recorded speech across many languages. We identify a statistically discriminative set of topic keywords, and examine the relationship between overall word error rate (WER), keyword-specific detection performance, and topic identification (Topic ID) performance on the Fisher Spanish corpus. Building increasingly constrained systems — from copious to limited...
In many topic identification applications, supervised training labels are indirectly related to the semantic content of the documents being classified. For example, many topically distinct emails will all be assigned a single broad category label of “spam” or “not-spam”, and a two-class classifier will lack direct knowledge of the underlying topic structure. This paper examines the degradation of...
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