Classifying Hypernasality for Children with a Cleft Palate using a Convolutional Neural Network

Abstract: In Sweden, about 150-200 children annually are born with some form of cleft lip and/or palate, making it the most common facial malformation in the country. Treatment often involves one or more surgeries and the speech development is followed up by a speech pathologist from the first year of life. One of the most common speech deviation for children with a cleft palate is hypernasality, which comes from the condition velopharyngeal insufficiency (VPI), which means that the soft palate cannot close the passage between the throat and nasal cavities properly. To classify the severity of VPI and hypernasality is hard, since listeners have different internal standards. This makes it important to develop an independent method to classify the severity. This thesis studied two existing deep learning methods from another master’s thesis on new data to see if it would be able to classify the severity of VPI, which is classified on a three point scale. The methods did not work well on the new data, but could be improved a bit by better data processing and some changes in the methods. The best performance for classifying VPI was a VGGish network which gave a file-wise accuracy of 57.11 %. A method to try and classify hypernasality was investigated as well. The best method found in order to classify hypernasality, which is classified on a four point scale, was to look at the vowels and to use mel spectrograms in a Convolutional Neural Network (CNN). Nasometry data was also given and together with the mel spectrograms an accuracy of 52.38 % was reached.