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In this paper, we present a word topic model (WTM) approach, discovering the co-occurrence relationship between words as well as the long-span latent topic information, for spoken document retrieval (SDR). A given document as a whole is modeled as a composite WTM model for generating an observed query. The underlying characteristics and different kinds of model structures are extensively investigated,...
This paper considers training data selection for discriminative training of acoustic models for broadcast news speech recognition. Three novel data selection approaches were proposed. First, the average phone accuracy over all hypothesized word sequences in the word lattice of a training utterance was utilized for utterance-level data selection. Second, phone-level data selection based on the difference...
Extractive summarization usually automatically selects indicative sentences from a document according to a certain target summarization ratio, and then sequences them to form a summary. In this paper, we investigate the use of information from relevant documents retrieved from a contemporary text collection for each sentence of a spoken document to be summarized in a probabilistic generative framework...
The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures...
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