For efficient organization of speech recordings – meetings, interviews, voice mails, lectures – the ability to search for spoken keywords is an essential capability. Today, most spoken-document retrieval systems use large-vocabulary recognition. For the above scenarios, such systems suffer from both the unpredictable vocabulary/ domain and generally high word-error rates (WER). In this paper, we present a vocabulary-independent system to index and rapidly search spontaneous speech. A speech recognizer generates lattices of phonetic word fragments, against which keywords are matched phonetically. We will first show the need to use recognition alternatives (lattices) in a high-WER context, on a word-based baseline. Then we will introduce our new method of phonetic word-fragment lattice generation, which uses longer-span language knowledge than a phoneme recognizer. Last we will introduce heuristics to compact the lattices to feasible sizes that can be searched efficiently. On the LDC Voicemail corpus, we show t at vocabulary/domainindependent phonetic search is as accurate as a vocabulary/domain- dependent word-lattice based baseline system for invocabulary keywords (FOMs of 74-75%), but nearly maintains this accuracy also for OOV keywords.

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Publisher: Institute of Electrical and Electronics Engineers, Inc.
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