  A key barrier to making phonetic studies scalable and replicable is the need
to rely on subjective, manual annotation. To help meet this challenge, a
machine learning algorithm was developed for automatic measurement of a widely
used phonetic measure: vowel duration. Manually-annotated data were used to
train a model that takes as input an arbitrary length segment of the acoustic
signal containing a single vowel that is preceded and followed by consonants
and outputs the duration of the vowel. The model is based on the structured
prediction framework. The input signal and a hypothesized set of a vowel's
onset and offset are mapped to an abstract vector space by a set of acoustic
feature functions. The learning algorithm is trained in this space to minimize
the difference in expectations between predicted and manually-measured vowel
durations. The trained model can then automatically estimate vowel durations
without phonetic or orthographic transcription. Results comparing the model to
three sets of manually annotated data suggest it out-performed the current gold
standard for duration measurement, an HMM-based forced aligner (which requires
orthographic or phonetic transcription as an input).
