| Paper: | SP-L11.4 | ||
| Session: | Language Modeling and Search | ||
| Time: | Friday, May 21, 16:30 - 16:50 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Large Vocabulary Recognition/Search | ||
| Title: | VOCABULARY-INDEPENDENT SEARCH IN SPONTANEOUS SPEECH | ||
| Authors: | Frank Seide; Microsoft Research Asia | ||
| Peng Yu; Microsoft Research Asia | |||
| Chengyuan Ma; Microsoft Research Asia | |||
| Eric Chang; Microsoft Research Asia | |||
| Abstract: | 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 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 OOV keywords. | ||
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