Vocabulary-independent search in spontaneous speech

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). We present a vocabulary-independent system to index and to search rapidly spontaneous speech. A speech recognizer generates lattices of phonetic word fragments, against which keywords are matched phonetically. We first show the need to use recognition alternatives (lattices) in a high-WER context, on a word-based baseline. Then we introduce our new method of phonetic word-fragment lattice generation, which uses longer-span language knowledge than a phoneme recognizer. Last we introduce heuristics to compact the lattices to feasible sizes that can be searched efficiently. On the LDC voice mail corpus, we show that vocabulary/domain-independent phonetic search is as accurate as a vocabulary/domain-dependent word-lattice based baseline system for in-vocabulary keywords (FOMs of 74-75%), but nearly maintains this accuracy also for out-of-vocabulary keywords.