Towards Spoken-Document Retrieval for the Internet: Lattice Indexing For Large-Scale Web-Search Architectures

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 415–422, New York, June 2006. c⃝2006 Association for Computational Linguistics Towards Spoken-Document Retrieval for the Internet: Lattice Indexing For Large-Scale Web-Search Architectures Zheng-Yu Zhou∗, Peng Yu, Ciprian Chelba+, and Frank Seide ∗Chinese University of Hong Kong, Shatin, Hong Kong Microsoft Research Asia, 5F Beijing Sigma Center, 49 Zhichun Road, 100080 Beijing +Microsoft Research, One Microsoft Way, Redmond WA 98052 zyzhou@se.cuhk.edu.hk, {rogeryu,chelba,fseide}@microsoft.com Abstract Large-scale web-search engines are generally designed for linear text. The linear text repre- sentation is suboptimal for audio search, where accuracy can be significantly improved if the search includes alternate recognition candi- dates, commonly represented as word lattices. This paper proposes a method for indexing word lattices that is suitable for large-scale web-search engines, requiring only limited code changes. The proposed method, called Time-based Merging for Indexing (TMI), first converts the word lattice to a posterior-probability represen- tation and then merges word hypotheses with similar time boundaries to reduce the index size. Four alternative approximations are pre- sented, which differ in index size and the strict- ness of the phrase-matching constraints. Results are presented for three types of typi- cal web audio content, podcasts, video clips, and online lectures, for phrase spotting and rel- evance ranking. Using TMI indexes that are only five times larger than corresponding linear- text indexes, phrase spotting was improved over searching top-1 transcripts by 25-35%, and rel- evance ranking by 14%, at only a small loss compared to unindexed lattice search. 1 Introduction Search engines have become the essential tool for find- ing and accessing information on the Internet. The re- cent runaway success of podcasting has created a need for similar search capabilities to find audio on the web. As more news video clips and even TV shows are offered for on-demand viewing, and educational institutions like MIT making lectures available online, a need for audio search arises as well, because the most informative part of many videos is its dialogue. There is still a significant gap between current web au- dio/video search engines and the relatively mature text search engines, as most of today’s audio/video search en- gines rely on the surrounding text and metadata of an au- dio or video file, while ignoring the actual audio content. This paper is concerned with technologies for searching the audio content itself, in particular how to represent the speech content in the index. Several approaches have been reported in the litera- ture for indexing spoken words in audio recordings. The TREC (Text REtrieval Conference) Spoken-Document Retrieval (SDR) track has fostered research on audio- retrieval of broadcast-news clips. Most TREC bench- marking systems use broadcast-news recognizers to gen- erate approximate transcripts, and apply text-based infor- mation retrieval to these. They achieve retrieval accuracy similar to using human reference transcripts, and ad-hoc retrieval for broadcast news is considered a “solved prob- lem” (Garofolo, 2000). Noteworthy are the rather low word-error rates (20%) in the TREC evaluations, and that recognition …

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