The Mason-Alberta Phonetic Segmenter: A forced alignment system based on
  deep neural networks and interpolation
Matthew C. Kelley Scott James Perry Benjamin V. Tucker
Forced alignment systems automatically determine boundaries between segments
in speech data, given an orthographic transcription. These tools are
commonplace in phonetics to facilitate the use of speech data that would be
infeasible to manually transcribe and segment. In the present paper, we
describe a new neural network-based forced alignment system, the Mason-Alberta
Phonetic Segmenter (MAPS). The MAPS aligner serves as a testbed for two
possible improvements we pursue for forced alignment systems. The first is
treating the acoustic model in a forced aligner as a tagging task, rather than
a classification task, motivated by the common understanding that segments in
speech are not truly discrete and commonly overlap. The second is an
interpolation technique to allow boundaries more precise than the common 10 ms
limit in modern forced alignment systems. We compare configurations of our
system to a state-of-the-art system, the Montreal Forced Aligner. The tagging
approach did not generally yield improved results over the Montreal Forced
Aligner. However, a system with the interpolation technique had a 27.92%
increase relative to the Montreal Forced Aligner in the amount of boundaries
within 10 ms of the target on the test set. We also reflect on the task and
training process for acoustic modeling in forced alignment, highlighting how
the output targets for these models do not match phoneticians' conception of
similarity between phones and that reconciliation of this tension may require
rethinking the task and output targets or how speech itself should be
segmented.