  Objectives: Evaluation of hypernasality requires extensive perceptual
training by clinicians and extending this training on a large scale
internationally is untenable; this compounds the health disparities that
already exist among children with cleft. In this work, we present the objective
hypernasality measure (OHM), a speech analytics algorithm that automatically
measures hypernasality in speech, and validate it relative to a group of
trained clinicians. Methods: We trained a deep neural network (DNN) on
approximately 100 hours of a publicly-available healthy speech corpus to detect
the presence of nasal acoustic cues generated through the production of nasal
consonants and nasalized phonemes in speech. Importantly, this model does not
require any clinical data for training. The posterior probabilities of the deep
learning model were aggregated at the sentence and speaker-levels to compute
the OHM.
  Results: The results showed that the OHM was significantly correlated with
the perceptual hypernasality ratings in the Americleft database ( r=0.797,
~p$<$0.001), and with the New Mexico Cleft Palate Center (NMCPC) database
(r=0.713,p<$0.001). In addition, we evaluated the relationship between the OHM
and articulation errors; the sensitivity of the OHM in detecting the presence
of very mild hypernasality; and establishing the internal reliability of the
metric. Further, the performance of OHM was compared with a DNN regression
algorithm directly trained on the hypernasal speech samples. Significance: The
results indicate that the OHM is able to rate the severity of hypernasality on
par with Americleft-trained clinicians on this dataset.
