## 9 Experimental Assessment and Analysis
### Experiment 1: Analysis of the Transitions Generated by the Fuzzy-Genetic Model  
The six phonetic classes considered in our system are the following: \(vowels(VO)\), \(plosive(PL)\), \(fricatives(FR)\), \(affricates(AF)\), \(liquid(LI)\), \(nasals(NA)\). Of course there are other classes, but we think that these six classes can be sufficient to cover most of the western languages. In theory, the six classes lead to thirty-six types of transitions between classes, like VO-PL or FR-VO; considering the transitions between phonemes, in theory we have 7560 transitions. However, many of them have been excluded, because or impossible or rarely used, so the resulting number is reduced to one hundred and ninety two (192). For the sake of completeness we report below the list of transitions used in our algorithm, where V stands for vowel and C for consonant. * 70 elements /_CV/_, that is, such as _/ba/_ or _/ka/_;
* 70 elements /_VC/_, that is, such as _/ap/_ or _/as/_;
* 10 elements /_\(\overline{\textit{CV}}\)_/, which are transitions between a, such as /t/a/;
* 10 elements /_\(\overline{\textit{VC}}\)_/, which are transitions between a vowel and an affricate;
* 8 elements /_VV/_, which are diphthongs;
* 14 elements /_-C/_, which represent transitions between silence and a consonant;
* 5 elements /_V-_/, which are transition between vowels and silence;
* 5 elements /_-V/_, which are transition between vowels and silence. For each transition the human tutor utters a very short utterance, each containing only one transition. This operation is similar to the Knowledge Base Acquisition (KBA) discussed in Section 4. The difference from KBA is that in that case the tutor utters a suitable number of longer words containing all the one hundred ninety two transitions. The longer word is then automatically segmented into diphones, so all the diphones are extracted from a phonetic context, so that during synthesis the best diphone is selected. In this experiment we just test the quality of the phonetic tracks, leaving to the KBA phase the task of acquiring the articulatory data which will be used for generating the articulatory movements. The analysis of the quality of the generated transitions is performed following these five steps:  
1. Generation of the synthetic version of each transition using the \(Fuzzy-Genetic\) algorithm. In this way each transition, say, between the plosive /_p_/ and the vowel /_a_/, namely the /_pa_/ diphone, has a naturally spoken version /_pa_/ and a synthetic version /_\(\overline{\textit{pa}}\)_/. 2. Phonetic speech alignment of all the segments with the same transition, for instance align /_pa_/ and /_\(\overline{\textit{pa}}\)_/ or /_at_/ with /_\(\overline{\textit{at}}\)_/ and so on using the DTW algorithm which is simple and efficient for this purpose [62]. This operation synchronizes the phonetic content of the natural and synthetic segments related to the same transition. 3. Application of the built-in formant tracker of the \(Praat\) tool [63], to each segment. The \(Praat\) tracker uses the \(Burg\) algorithm for formant extraction [64]
4. Generate the \(GroundTruth\) of the second formant tracks by manually inspecting the spectrograms of the one hundred ninety two spoken segments. 5. Compute the quality measure of the formant trajectories extracted from each diphone, synthetic and natural, by comparing them with the \(GroundTruth\). The quality measure is defined as follows. First we compute the three errors described in (4), (5) and (6).