  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 quantal non-linearities in the articulatory-acoustic
and acoustic-perceptual relations to define landmark times at which the speech
spectrum abruptly changes or reaches an extremum; frames overlapping landmarks
have been demonstrated to be sufficient for speech perception. In this work, we
conduct experiments on the TIMIT corpus, with both GMM and DNN based ASR
systems and find that frames containing landmarks are more informative for ASR
than others. We find that altering the level of emphasis on landmarks by
re-weighting acoustic likelihood 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 optimal when scoring
less than half (45.8% to be precise) of the frames. This hybrid strategy
out-performs other non-heuristic-based methods and demonstrate the potential of
landmarks for reducing computation.
