# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 4 Discussion  
Two were the main objectives of this study. In the first place we aimed to determine whether or not it was possible to detect HN with utterances more complex than sustained sounds and, in that case, which were such utterances. A second aim was to test the feasibility of using mobile devices to detect HN. To this end we created a database of speech samples obtained with the help of a mobile app. From this database we excluded the patients for which there was no evidence of HN. It is important that the requisite to include the patients (i.e., individual-by-individual basis) implies that some of the utterances in the nasal group might be non-nasal. Given that this methodological decision might be considered a potential limitation we will begin the discussion with this issue. Then, we will discuss the main results. The decision to select the participants on an individual-by-individual basis, rather than utterance-by-utterance, was motivated by practical considerations: it is clearly less time-consuming than the alternative approach. Our approach might have reduced the accuracy of the classifiers because many non-nasalized utterances were included in the patient's database. However, the relatively good results in terms of accuracy indicate that the approach was a valid one. Two factors may have contributed to our results. One is the \begin{table}
\begin{tabular}{c c c c} \hline
**Utterances** & **Alg.** & **Results** & **Features** \\ \hline _T5 dedo_ & & Spec.: 92\% & _T5 dedo_: bwf1, f3, f1-f3, f2-f3 \\ _+T3 Eff_ & SVM & Sens.: 92\% & _T3 Eff_: mfcc2, mfcc10, fcc13 \\ _+T2 pa_ & & Acc.: 92\% & _T2 pa_: mfcc1, mfcc5, mfcc9 \\ \hline _T5 dedo_ & SVM & Spec.: 85\% & bwf1, bwf2, f3, f1-f3, f2-f3 \\  & & Sens.: 77\% & mfcc2, mfcc5, mfcc11, mfcc12 \\  & & Acc.: 81\% & vlhr400, vlhr500, vlhr900 \\ \hline _T2 ka_ & SVM & Spec.: 73\% & bwf1, bwf3, f1, f3, f2-f3, f2-f3, \\  & & Sens.: 85 & mfcc1, mfcc3, mfcc4, mfcc6, mfcc7, mfcc10, \\  & & Acc.: 79\% & mfcc11, vlhr900 \\ \hline _T2 ki_ & RF & Spec.: 75\% & bwf1, bwf2, fowf3, f1, \\  & & Sens.: 83\% & mfcc1, mfcc2, mfcc4, mfcc6, mfcc7, \\  & & Acc.: 79\% & mfcc11, mfcc12, vlhr800, vlhr900 \\ \hline _T2 pi_ & RF & Spec.: 79\% & bwf1, bwf3, f2, f3, f1-f3, f2-f3 \\  & & Sens.: 79\% & mfcc1, mfcc2, mfcc3, mfcc5, mfcc6, mfcc8, \\  & & Acc.: 79\% & mfcc12, vlhr600, vlhr700 \\ \hline _T2 ta_ & RF & Spec.: 75\% & bwf1, bwf2, f3, f1-f3, f2-f3 \\  & & Sens.: 80\% & mfcc1, mfcc2, mfcc4, mfcc5, mfcc6, mfcc7, \\  & & Acc.: 77\% & mfcc8, mfcc9, vlhr400, vlhr500, vlhr600 \\ \hline _T5 pez_ & SVM & Spec.: 77\% & bwf1, bwf3, f3, f1-f3, f2-f3 \\  & & Sens.: 77\% & mfcc2, mfcc4, mfcc5, mfcc6, mfcc8 \\  & & Acc.: 77\% & vlhr400 \\ \hline _T6 A David_ & SVM & Spec.: 77\% & bwf1, bwf3, f2, f1-f2, f2-f3, \\  & & Sens.: 74\% & mfcc2, mfcc4, mfcc5, mfcc7, mfcc11, \\  & & Ass.: 76\% & mfcc12 \\  & & Acc.: 76\% & vlhr400, vlhr500, vlhr600 \\ \hline \end{tabular}
\end{table} 
Table 3: Utterances and classifiers used in the HN Score (_7 + Sel_). possibility that patients' HN persists in some utterances to such a minimal degree that the human ear is not capable of recognizing it. The other is that, as noted in clinical research, there are contexts which favor HN and the feature selection and reduction process may have discarded the utterances that do not favor HN. Our results show that syllables sequences and, to a lesser extent, specific sentences, words and sustained consonants, are the most appropriate types of utterances to evaluate HN automatically. In contrast, the accuracy obtained by using rotten speech (i.e., counting one to ten) or sustained vowels is relatively low. In order to explain these results it may be helpful to compare the accuracy of three groups of utterances: (1) the /pa ta ka/ series versus the sustained vowel /a/; (2) rotten speech versus sentences; and (3) the sustained vowel /a/ versus the sustained consonant /f/.