MARTA: a model for the automatic phonemic grouping of the parkinsonian speech

Abstract

Parkinson’s disease significantly impacts speech, particularly affecting phonemic groups like stop-plosives, fricatives, and affricates. However, its objective impact on the different phonemic groups has been briefly addressed in the past. This study introduces a new model, called MARTA, built upon a Gaussian Mixture Variational AutoEncoder with metric learning to measure the disease’s impact on the phonemic grouping automatically and objectively. MARTA was trained on normophonic speech before adapting it to parkinsonian speech. The model effectively clusters phonemic groups unsupervised and demonstrates enhanced discriminative power when supervised using forced-aligned labels. Our findings reveal that beyond the traditionally affected phonemes, Parkinson’s disease not only affects stop-plosives, voiced-plosives, and nasals, but also significantly impacts liquids, vowels, and fricatives, with the model achieving a benchmarking 91% ± 9 discrimination capability. An in-depth evaluation of the impact of the disease on the different phonemic groups represents an advance in the current knowledge of its effects on the speech, and has clear implications in the speech therapy of people with Parkinson’s disease. Moreover, regardless of the specific application domain presented, the model introduced has potential downstream utility in assessing the manner of articulation, whether influenced by other medical conditions or certain dialectal variations.