# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 4 Discussion
However, our results also show that a better approach to capture the complexity of HN consists in combining the results of multiple classifiers. Using the HN Score we observed that combining the results of a small list of highly accurate classifiers allows not only to discriminate the two classes, but also to discard speakers for which the automatic classifiers provided conflicting results. This result shows that it is more effective to evaluate HN based on multiple utterances than based on a single utterance. A secondary aim of this study was to determine the feasibility of using mobile devices to detect HN automatically. This approach presents at least two potential limitations: one related to the participants' implication in the task and another related to the acoustic context. Regarding the participants' implication, this may arise due to multiple circumstances (limited attention, lack of motivation, etc.) However, the results are promising as they show that the majority of the participants completed the task. The only exception were children aged three or four, for whom the task may have been too difficult or too long. For this group it seems that it might be appropriate to develop a shorter task, for which the results described above provide some alternatives (e.g., using exclusively syllable repetition, or a limited number of utterances such as the ones included in Table 3). Regarding the acoustic context, one potential problem was the presence of noise (e.g., from other speakers, electronic devices such as computers or air conditioners, cars) This might be particularly relevant for patients whose respiratory weakness can make their voice less audible than that of controls. However, the results indicate that this has not been an important limitation: the HN Scores were slightly lower in the patients with background noise than in the patients without background noise, which shows that the background of noise had a limited impact on the results. Altogether our results indicate that it is feasible to use mobile devices to make an automatic assessment of HN. Our results point to some issues that require further exploration. In the first place, the results of this study suggest that patients and controls differed notably in how they produced syllables sequences and the sustained consonant /f/. However, we could not clarify whether these differences were due to the presence of nasal resonance or, alternatively, to adaptations in the articulatory patterns used to produce these sounds. Clarifying this issue might be most valuable to have better understanding of the speech characteristics of hypernasal speakers. In the second place, regarding the possibility of using mobile devices in speech evaluation, two limitations must be noted. In the first place, we did not carry a comparison between data obtained with our app and data obtained with other recording techniques. Unfortunately, due to the COVID-19 crisis, we were not able to obtain such data. In the second place, it should be emphasized that, in this study, we used devices of relatively high quality (i.e., iPhone and iPad). Thus, it is necessary to explore to what extent the results are the same independently of the recording tools used and whether or not the accuracy of the different classifiers remains equally high when using devices of a lower quality. Finally, future studies should explore whether or not the methodology used in this study can serve to grade the severity of HN and also to detect changes associated to medical and speech therapy treatments.