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Table 1: Test accuracy (%) for different CTRNN architectures on phonetic vs. word representation.
['Model characteristic', 'EMILv1\xa0data Phonetic', 'EMILv1\xa0data Word embedding', 'EMILv1\xa0+\xa0Teacher data Phonetic', 'EMILv1\xa0+\xa0Teacher data Word embedding']
We observe that the generalisation is difficult for all models and that utterances which were described entirely correct are rare. For the phonetic representation, the model produces descriptions with a range of small errors such as pauses that are too long or producing incorrect phonemes at the end of words (rare) or of the utterance (more common). In many of those cases, the model shows tendencies to produce wrong descriptions from the first incorrect phoneme onward. For the word embedding representation, the descriptions are overall better, but in some cases, words are mixed up that are not necessarily semantically related.