  Diadochokinetic speech tasks (DDK), in which participants repeatedly produce
syllables, are commonly used as part of the assessment of speech motor
impairments. These studies rely on manual analyses that are time-intensive,
subjective, and provide only a coarse-grained picture of speech. This paper
presents two deep neural network models that automatically segment consonants
and vowels from unannotated, untranscribed speech. Both models work on the raw
waveform and use convolutional layers for feature extraction. The first model
is based on an LSTM classifier followed by fully connected layers, while the
second model adds more convolutional layers followed by fully connected layers.
These segmentations predicted by the models are used to obtain measures of
speech rate and sound duration. Results on a young healthy individuals dataset
show that our LSTM model outperforms the current state-of-the-art systems and
performs comparably to trained human annotators. Moreover, the LSTM model also
presents comparable results to trained human annotators when evaluated on
unseen older individuals with Parkinson's Disease dataset.

Audio and Speech Processing;Machine Learning;Sound