2 research outputs found
Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features
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.Comment: 12 pages, 9 figures, 2 table
A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children with Cleft Palate
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,
~p0.001), 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