  Hypernasality is a common characteristic symptom across many motor-speech
disorders. For voiced sounds, hypernasality introduces an additional resonance
in the lower frequencies and, for unvoiced sounds, there is reduced
articulatory precision due to air escaping through the nasal cavity. However,
the acoustic manifestation of these symptoms is highly variable, making
hypernasality estimation very challenging, both for human specialists and
automated systems. Previous work in this area relies on either engineered
features based on statistical signal processing or machine learning models
trained on clinical ratings. Engineered features often fail to capture the
complex acoustic patterns associated with hypernasality, whereas metrics based
on machine learning are prone to overfitting to the small disease-specific
speech datasets on which they are trained. Here we propose a new set of
acoustic features that capture these complementary dimensions. The features are
based on two acoustic models trained on a large corpus of healthy speech. The
first acoustic model aims to measure nasal resonance from voiced sounds,
whereas the second acoustic model aims to measure articulatory imprecision from
unvoiced sounds. To demonstrate that the features derived from these acoustic
models are specific to hypernasal speech, we evaluate them across different
dysarthria corpora. Our results show that the features generalize even when
training on hypernasal speech from one disease and evaluating on hypernasal
speech from another disease (e.g. training on Parkinson's disease, evaluation
on Huntington's disease), and when training on neurologically disordered speech
but evaluating on cleft palate speech.
