## 4 Discussion
### Limitations and Future Work  
Although the amount of prosodic productions analyzed was large (605 utterances, as shown in Table 5), the number of informants, especially in C1 subcorpus, is low. We have reported statistically significant correlations by using these samples, but the statistical power of these results needs to be strengthened with future recordings of more participants. Nevertheless, the sample size has not prevented us from fulfilling the first aim of the paper. Thus, an accurate automatic classification system with a low rate of false negatives was successfully developed. Moreover, the criterion for sample selection (see Section 2.2) by which teachers were asked to choose individuals with Down syndrome of different developmental levels ensured that the sample was representative of the variability inherent to the population of individuals with Down syndrome. This allowed us to analyze how the heterogeneity of these individuals can affect the assessment results and therefore reach our second aim. Even so, it should be noted that further research should compile a bigger and more balanced corpus of the speech of individuals with Down syndrome and should also record a reference corpus of people with typical development. A bigger corpus will have to be compiled in order to explore new approaches such as end-to-end deep learning methods, which have shown promising results in the assessment of atypical prosody in other populations with intellectual disabilities, such as Autism Spectrum Disorder [47].  
The differences between the therapist and prosody expert evaluations highlight the importance of evaluation contexts. If the automatic evaluation module aims to be included in a real time video game, aspects different from prosody should also be considered, in the line of what the therapist did in her evaluation. In addition, the evaluation scale can be improved by adding more dimensions to be scored by the experts. Instead of having a global score of the prosody of a recording, the experts could assign a different score to different prosodic dimensions (intonation, accent, phrasing), with the aim of making a more precise classification. These features could then also be automatically classified.  
As already mentioned, the fact that 10.1% of the samples evaluated as Right by the expert are classified as Wrong by the classifier is a good result, because evaluating a recording as Wrong when the recording was Right can affect the motivation of the player. Yet, reducing this rate of false negatives in order to obtain the best possible reliable evaluation system is work in progress. To reach this goal, inter-speaker variability should be taken into account as an intrinsic feature of individuals with Down syndrome, so that both the reference for correct pronunciation and the particular limitations of the speaker should be taken into account to ensure an effective automatic prosodic assessment.  
Knowing the variables that contribute to variability in the quality of the prosodic productions of individuals with Down syndrome paves the way for the design of the best possible automatic classification system for the PRADIA video game as an intervention tool, in particular, or for other future intervention programs, in general. Having such an automatic classification module would allow the player to have more autonomy, which in turn would have a positive impact on his/her self-confidence and motivation. At the same time, the automatic classification module would release resources for the therapist, who could use the time needed to support individuals with Down syndrome in the PRADIA intervention tool for other intervention activities. Therefore, our results represent a first step for the future development of useful intervention materials. Thus, our results could benefit future clinical practices.