Abstract

There are many studies that identify important deficits in the voice production of people with Down syndrome. These deficits affect not only the spectral domain, but also the intonation, accent, rhythm and speech rate. The main aim of this work is the identification of the acoustic features that characterize the speech of people with Down syndrome, taking into account the different frequency, energy, temporal and spectral domains. The comparison of the relative weight of these features for the characterization of Down syndrome people’s speech is another aim of this study. The openSmile toolkit with the GeMAPS feature set was used to extract acoustic features from a speech corpus of utterances from typically developing individuals and individuals with Down syndrome. Then, the most discriminant features were identified using statistical tests. Moreover, three binary classifiers were trained using these features. The best classification rate, using only spectral features, is 87.33%, and using frequency, energy and temporal features, it is 91.83%. Finally, a perception test has been performed using recordings created with a prosody transfer algorithm: the prosody of utterances from one group of speakers was transferred to utterances of another group. The results of this test show the importance of intonation and rhythm in the identification of a voice as non typical. As conclusion, the results obtained point to the training of prosody in order to improve the quality of the speech production of those with Down syndrome.

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