AIx Speed: Playback Speed Optimization Using Listening Comprehension of
  Speech Recognition Models
Kazuki Kawamura Jun Rekimoto
Since humans can listen to audio and watch videos at faster speeds than
actually observed, we often listen to or watch these pieces of content at
higher playback speeds to increase the time efficiency of content
comprehension. To further utilize this capability, systems that automatically
adjust the playback speed according to the user's condition and the type of
content to assist in more efficient comprehension of time-series content have
been developed. However, there is still room for these systems to further
extend human speed-listening ability by generating speech with playback speed
optimized for even finer time units and providing it to humans. In this study,
we determine whether humans can hear the optimized speech and propose a system
that automatically adjusts playback speed at units as small as phonemes while
ensuring speech intelligibility. The system uses the speech recognizer score as
a proxy for how well a human can hear a certain unit of speech and maximizes
the speech playback speed to the extent that a human can hear. This method can
be used to produce fast but intelligible speech. In the evaluation experiment,
we compared the speech played back at a constant fast speed and the flexibly
speed-up speech generated by the proposed method in a blind test and confirmed
that the proposed method produced speech that was easier to listen to.