Exploring Effective Pseudo-Relevance Feedback and Proximity Information for Speech Retrieval and Transcription

• Main Author: 陳憶文
• Other Authors: Berlin Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/24695216658836083699

碩士 === 國立臺灣師範大學 === 資訊工程學系 === 101 === Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation in spoken document retrieval, which assumes that a small amount of top-ranked feedback documents obtained from the initial retrieval are relevant and can be utilized for query expansion. Nevertheless, simply taking all of the top-ranked feedback documents acquired from the initial retrieval for query modeling does not necessary work well, especially when the top-ranked documents contain much redundant or non-relevant cues. In view of this, we explore different kinds of information cues for selecting helpful feedback documents to further improve information retrieval. On the other hand, relevance model (RM) based on “bag-of-words” assumption, which can facilitate the derivation and estimation, may be oversimplified for the task of language modeling in speech recognition. Hence, we also enhance RM in two significant aspects. First, “bag-of-words” assumption of RM is relaxed by incorporating word proximity information into RM formulation. Second, topic-based proximity information is additionally explored to further enhance the proximity-based RM framework. Experiments conducted on not only a spoken document retrieval task but also a speech recognition task indicates that our approaches can bring competitive utilities to existing ones.