English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Objectives: Evaluation of hypernasality requires extensive perceptualtraining by clinicians and extending this training on a large scaleinternationally is untenable; this compounds the health disparities thatalready exist among children with cleft. In this work, we present the objectivehypernasality measure (OHM), a speech analytics algorithm that automaticallymeasures hypernasality in speech, and validate it relative to a group oftrained clinicians. Methods: We trained a deep neural network (DNN) onapproximately 100 hours of a publicly-available healthy speech corpus to detectthe presence of nasal acoustic cues generated through the production of nasalconsonants and nasalized phonemes in speech. Importantly, this model does notrequire any clinical data for training. The posterior probabilities of the deeplearning model were aggregated at the sentence and speaker-levels to computethe OHM.  Results: The results showed that the OHM was significantly correlated withthe 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 OHMand articulation errors; the sensitivity of the OHM in detecting the presenceof very mild hypernasality; and establishing the internal reliability of themetric. Further, the performance of OHM was compared with a DNN regressionalgorithm directly trained on the hypernasal speech samples. Significance: Theresults indicate that the OHM is able to rate the severity of hypernasality onpar with Americleft-trained clinicians on this dataset.

A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children with Cleft Palate