Spoken document retrieval using topic models

In this paper, we propose a document topic model (DTM) based on the non-negative matrix factorization (NMF) approach to explore spontaneous spoken document retrieval. The model uses latent semantic indexing to detect underlying semantic relationships within documents. Each document is interpreted as a generative topic model belonging to many topics. The relevance of a document to a query is expressed by the probability of a query being generated by the model. The term-document matrix used for NMF is built stochastically from the speech recognition N-best results, so that multiple recognition hypotheses can be utilized to compensate for the word recognition errors. Using this approach, experiments are conducted on a test collection from the Corpus of Spontaneous Japanese (CSJ), with 39 queries for over 600 hours of spontaneous Japanese speech. The retrieval performance of this model is proved to be superior to the conventional vector space model (VSM) when the dimension or topic number exceeds a certain threshold. Moreover, whether from the viewpoint of retrieval performance or the ability of topic expression, the NMF-based topic model is verified to surpass another latent indexing method that is based on the singular value decomposition (SVD). The extent to which this topic model can resist speech recognition error, which is a special problem of spoken document retrieval, is also investigated.