# Unmasking Nasality to Assess Hypernasality
###### Abstract  
Automatic evaluation of hypernasality has been traditionally computed using monophonic signals (i.e., combining nose and mouth signals). Here, this study aimed to examine if nose signals serve to increase the accuracy of hypernasality evaluation. Using a conventional microphone and a Nasometer, we recorded monophonic, mouth, and nose signals. Three main analyses were performed: (1) comparing the spectral distance between oral/nasalized vowels in monophonic, nose, and mouth signals; (2) assessing the accuracy of Deep Neural Network (DNN) models in classifying oral/nasal sounds and vowel/consonant sounds trained with nose, mouth, and monophonic signals; (3) analyzing the correlation between DNN-derived nasality scores and expert-rated hypernasality scores. The distance between oral and nasalized vowels was the highest in the nose signals. Moreover, DNN models trained on nose signals outperformed in nasal/oral classification (accuracy: 0.90), but were slightly less precise in vowel/consonant differentiation (accuracy: 0.86) compared to models trained on other signals. A strong Pearson's correlation (0.83) was observed between nasality scores from DNNs trained with nose signals and human expert ratings, whereas those trained on mouth signals showed a weaker correlation (0.36). We conclude that mouth signals partially mask the nasality information carried by nose signals. Significance: the accuracy of hypernasality assessment tools may improve by analyzing nose signals. clinical speech analysis; deep neural networks; hypernasality; speech assessment; vocal biomarkers +
Footnote †: journal: _Sci. evaluate hypernasality [4]. In the last quarter of the 20 th century, one acoustic instrument, the Nasometer, was proposed to supplement human judgments [5]. The Nasometer records the nose and mouth acoustic signals separately, which makes it possible to compute the nasalance, which is defined as the ratio of nasal acoustic energy to the sum of oral and nasal. The Nasometer has had relative success in clinical practice [4; 6]. However, the reliability of this instrument is unclear; studies comparing the nasalance scores and perceptual scores have obtained correlations ranging from non-significant to strong (for a review see [7]). In the last two decades, motivated by advances in speech processing technology and artificial intelligence, several proposals were made that combine novel acoustic features and machine learning algorithms [8]. One issue that has received little attention is the type of target signal for these analyses. To our knowledge, all previous studies have used standard monophonic signals, which are the result of combining (coupling) the two signals produced by humans (i.e., mouth and nose signals). However, as shown by nasalance studies, it is also possible to analyze these signals separately. In the present study, we examine to what extent using either the mouth or the nose signal exclusively might be a better alternative to evaluate hypernasality than the traditional monophonic signal. Note that if this possibility is confirmed, it might have some practical implications. On the one hand, it might serve as an approach to increase the accuracy of advanced speech processing algorithms. On the other hand, it might point to a potential alternative to the Nasometer, a device that is notably expensive (>1500 USD in many countries), and hence unaffordable to many audiologists and speech therapists around the world. There are some indications that nose signals might be an optimal target to explore nasality. For instance, a recent study [9] recorded the nose and mouth signals with a Nasometer, and simultaneously obtained a high-speed video of nasopharyngealopy; then, the author correlated the two acoustic signals with the video signal and the correlation was significant only in the case of the nose acoustic signal.