Interpretable speech features vs. DNN embeddings: What to use in the automatic assessment of Parkinson's disease in multi-lingual scenarios.

Speech-based approaches for assessing Parkinson's Disease (PD) often rely on feature extraction for automatic classification or detection. While many studies prioritize accuracy by using non-interpretable embeddings from Deep Neural Networks, this work aims to explore the predictive capabilities and language robustness of both feature types in a systematic fashion. As interpretable features, prosodic, linguistic, and cognitive descriptors were adopted, while x-vectors, Wav2Vec 2.0, HuBERT, and TRILLsson representations were used as non-interpretable features. Mono-lingual, multi-lingual, and cross-lingual machine learning experiments were conducted leveraging six data sets comprising speech recordings from various languages: American English, Castilian Spanish, Colombian Spanish, Italian, German, and Czech. For interpretable feature-based models, the mean of the best F1-scores obtained from each language was 81% in mono-lingual, 81% in multi-lingual, and 71% in cross-lingual experiments. For non-interpretable feature-based models, instead, they were 85% in mono-lingual, 88% in multi-lingual, and 79% in cross-lingual experiments. Firstly, models based on non-interpretable features outperformed interpretable ones, especially in cross-lingual experiments. Specifically, TRILLsson provided the most stable and accurate results across tasks and data sets. Conversely, the two types of features adopted showed some level of language robustness in multi-lingual and cross-lingual experiments. Overall, these results suggest that interpretable feature-based models can be used by clinicians to evaluate the deterioration of the speech of patients with PD, while non-interpretable feature-based models can be leveraged to achieve higher detection accuracy.

Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease.

Motor impairments are only one aspect of Parkinson's disease (PD), which also include cognitive and linguistic impairments. Speech-derived interpretable biomarkers may help clinicians diagnose PD at earlier stages and monitor the disorder's evolution over time. This study focuses on the multilingual evaluation of a composite array of biomarkers that facilitate PD evaluation from speech. Hypokinetic dysarthria, a motor speech disorder associated with PD, has been extensively analyzed in previously published studies on automatic PD evaluation, with a relative lack of inquiry into language and task variability. In this study, we explore certain acoustic, linguistic, and cognitive information encoded within the speech of several cohorts with PD. A total of 24 biomarkers were analyzed from American English, Italian, Castilian Spanish, Colombian Spanish, German, and Czech by conducting a statistical analysis to evaluate which biomarkers best differentiate people with PD from healthy participants. The study leverages conceptual robustness as a criterion in which a biomarker behaves the same, independent of the language. Hence, we propose a set of speech-based biomarkers that can effectively help evaluate PD while being language-independent. In short, the best acoustic and cognitive biomarkers permitting discrimination between experimental groups across languages were fundamental frequency standard deviation, pause time, pause percentage, silence duration, and speech rhythm standard deviation. Linguistic biomarkers representing the length of the narratives and the number of nouns and auxiliaries also provided discrimination between groups. Altogether, in addition to being significant, these biomarkers satisfied the robustness requirements.