Exploiting Machine Learning Methods for Spoken Document Retrieval

• Main Author: 游斯涵
• Other Authors: Berlin Chen
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/z2j5u7

碩士 === 國立臺灣師範大學 === 資訊工程研究所 === 97 === This thesis investigates the use of machine-learning approaches, namely learning-to-rank algorithms, for information retrieval (IR), with special emphasis on their theoretical foundations and the associated features that are used by them, such as the lexical features, proximity features, and probabilistic features. Meanwhile, we also consider the application of these approaches for spoken document retrieval (SDR). All experiments were conducted on the Topic Detection and Tracking corpora (especially, TDT-2 and TDT-3), which are the benchmark collections widely adopted for various SDR evaluations since they contain tens of hours of mainland-accented Chinese broadcast news documents equipped with topic labels and orthographic transcripts. In the hope of discovering more useful speech-related features for SDR as well as analyzing the problems caused by speech recognition errors, a large vocabulary speech recognition (LVCSR) system that can output a word lattice consisting of multiple recognition hypotheses for each broadcast news document is established. Moreover, we also deal with the problem of training the machine-learning retrieval models with unbalanced training data, and propose a remedy for it. Finally, the preliminary experimental results seem to show that the RankNet based retrieval model outperforms the support vector machine (SVM) based retrieval model for the SDR task studied in this thesis.