# Unmasking Nasality to Assess Hypernasality
### Automatic Assessment of Hypernasal Speech  
Many proposals have been made to evaluate hypernasality automatically, both in terms of the acoustic features used to process the speech signal and in terms of the automatic classification method. As for the features, these include spectral poles and zeros, increased spectral flatness, formant amplitude, the voice low-to-high tone ratio, and MFCCs [15; 16; 17; 18; 19; 20]. While the classifiers include Support Vector Machines (SVMs), Gaussian mixture models (GMMS), and Deep Neural Networks (DNNs) [18; 19; 21; 22]. Most studies have trained their classifiers using samples of healthy and hypernasal speech. However, this approach has two important limitations: (1) the size of the training database is relatively small (i.e., up to one or two hundred speakers producing a small number of utterances in most cases) and (2) most studies processed only sustained vowels [23; 24] or vowel fragments that had been annotated manually in words or sentences [25; 26; 27; 28]; this approach is not compatible with clinical protocols that emphasize the need to use diverse speech samples (e.g., running speech [29]).  
Recently, [PERSON] et al. [30] proposed an innovative approach (see Figure 2) that overcomes the two above-mentioned limitations. In their model, training was carried out with speech samples from healthy speakers, which allowed them to use one freely available large database to train a DNN model. Specifically, they used 100 h from the Librispeech corpus [31]. To train the system, the speech samples were divided into short windows from  
Figure 1: Long-term Average Spectrum of nasalized vowel /\(\tilde{\epsilon}\)/ (red lines) and oral vowel /\(\mathrm{e}\)/ (black lines), recorded simultaneously with three microphones.  
which MFCCs are computed and based on phonetic transcriptions, classified into one of four classes: oral consonant (OC), oral vowel (OV), nasal consonant (NC), and nasal vowel (NV). During the testing phase, the system was fed with new MFFC vectors, which are classified as belonging to one of the four classes. This allows computing the severity of nasality as the number of frames that are nasal divided by the total number of frames (per speaker or utterance). The results showed that the correlation between the scores of the DNN and those produced by Speech and Language Pathology (SLP) experts was very high (r = 0.80).  
One practical limitation of this approach is that to train their DNN, the authors used a large amount of training data (>100 h), and such large databases are available only for a very small number of languages. Despite such a limitation, it is relevant to compare their results with nasalance studies (as these also evaluate hypernasality based on running speech). By selecting only nasalance studies with a number of participants large enough to compute the Pearson correlation, we find that one study obtained a very high correlation of 0.88 [32], but in all other studies, the correlation is notably lower: 0.74 [33], 0.59 [34], and 0.55 [35]. Thus, it seems that the above-described DNN model might be a good alternative to the Nasometer. However, more studies are needed to confirm the reliability of this DNN model. It is also relevant to explore if the results are optimal using nose signals.