| 摘要 |
A technique for audio searches by statistical pattern matching is disclosed. The audio to be located is processed for feature extraction and decoded using a maximum likelihood ("ML") search. A left-right Hidden Markov Model ("HMM") is constructed from the ML state sequence. Transition probabilities are defined as normalized state occupancies from the most likely state sequence of the decoding operation. Utterance duration is measured from the search sample. Other model parameters are gleaned from an acoustic model. A ML search of an audio corpus is conducted with respect to the HMM and a garbage model. New start states are added at each frame. Low scoring and long state sequences (with respect to the search sample duration) are discarded at each frame. Locations where scores of the new model are higher than those of the garbage model are marked as potential matches. The highest scoring matches are presented as results.
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