# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 4 Discussion
As to first pair (i.e., syllable sequences vs. sustained vowel /a/), our results showed that the accuracy was relatively high in syllable sequences /pa ta ka/ and relatively low in /a/. This result indicates that the sustained vowels were very similar in the two groups, but the syllables were relatively different. In order to interpret these results, it is relevant to note that in the /pa ta ka/ series the same vowel is produced repeatedly; this means that, when produced by a healthy speaker, the average spectrum should be very similar to that of a vowel /a/. Thus, we interpret that patients are producing atypical syllable sequences. One possible interpretation of this result is that, due the increased effort required to produce the syllable sequences, the patients may struggle to control the velum, which may result in some degree of HN ([12]). However, it is also possible that the velopharyngeal insufficiency has led patients to modify slightly the articulatory patterns to produce these sounds (i.e., subtle compensatory mechanisms): these articulatory changes might modify the spectral configuration of the target sound in ways that may pass undetected to the human expert but that could be detected by the automatic classification system [24]. As to the second pair, rotten speech (i.e., counting one-to-ten) versus sentences, the accuracy of the former is clearly lower than that of some sentence (see Figure 4). Two factors may have contributed to this result. One is that numbers may have been practiced intensively by some children, for which at least some participants might be particularly effective in avoiding HN in this precise case. In contrast, the sentences may favor HN because they have multiple instances of the same consonant in different verbal contexts, all of which can impede the effective control of the velar closure. Another factor is that in the one-to-ten series, HN may occur occasionally (e.g., in one or two phonemes) and, thus, it may be blurred after averaging multiple window frames. In contrast, the phonological structure of some sentences may lead to relatively frequent instances of HN. As to the sustained consonant /f/, the results indicate that this utterance might be effective in discriminating the patients from the controls either alone or in combination with other utterances (see Table 3). This result is relevant because, in the clinical context, this consonant is used commonly to detect air scape in HN patients, but not to detect nasal resonance. However, the results shown in Table 3 indicate that the two groups differed significantly in at least three MFCC features, suggesting that there are spectral differences between the /f/ sounds produced by the patients and the ones produced by the controls. Two possible explanations can be suggested for this result, which are identical to the ones noted in the case of the syllable sequences: the effect might be caused by presence of nasal resonance or, alternatively, it might be associated with learned articulatory patterns aimed to compensate the difficulty to generate sufficient oral air pressure. Another relevant outcome of this study is that classifiers trained with between two and three utterances were more accurate than those using just one (see Table 2). This result extends those of [PERSON] et al. [17], who showed that classifying HN using individual vowels was less effective than using multiple vowel utterances. One possible explanation for these results is that HN is a complex phenomenon and that a single utterance type may not be sufficient to capture all the variation that can be observed among hypernasal patients. Note that the reduction in accuracy when the number of utterances increases is most possibly the result of the feature selection process, which did not take into account the correlation between the selected features. This may have led to select redundant features and to discard other features that might contribute to the classification problem.