Acoustic Landmarks Contain More Information About the Phone String than Other Frames
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Most mainstream Automatic Speech Recognition (ASR) systems consider all feature frames equally important. However, acoustic landmark theory is based on a contradictory idea, that some frames are more important than others. Acoustic landmark theory exploits the quantal nonlinear articulatory-acoustic relationships from human speech perception experiments, and provides theoretical support for extracting acoustic features in the vicinity of landmark regions where an abrupt change occurs in the spectrum of speech signals. In this work, we conduct experiments on the TIMIT corpus, with both GMM and DNN based ASR systems and found that frames containing landmarks are more informative than others. We found that altering the level of emphasis on landmarks through accordingly re-weighting acoustic likelihood in frames, tends to reduce the phone error rate (PER). Furthermore, by leveraging the landmark as a heuristic, one of our hybrid DNN frame dropping strategies maintained a PER within 0.44 of the frames. This hybrid strategy out-performs other non-heuristicbased methods and demonstrates the potential of landmarks for reducing computation.
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