Spoken Document Retrieval Leveraging Unsupervised and Supervised Topic Modeling Techniques

Kuan-Yu CHEN  Hsin-Min WANG  Berlin CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D    No.5    pp.1195-1205
Publication Date: 2012/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1195
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Recent Advances in Multimedia Signal Processing Techniques and Applications)
Category: Speech Processing
spoken document retrieval,  topic model,  supervised training,  pseudo-supervised training,  subword-level indexing,  

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This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches.