Feasibility Study of Parkinson's Speech Disorder Evaluation With Pre-Trained Deep Learning Model for Speech-to-Text Analysis.

Abstract

Objective: This study investigates the feasibility of employing a pre-trained deep learning wave-to-vec model for speech-to-text analysis in individuals with speech disorders arising from Parkinson's disease (PD).

Methods: A publicly available dataset containing speech recordings including the Hoehn and Yahr (H&Y) staging, Movement Disorder Society Unified Parkinson's Disease Rating Scale (UPDRS) Part I, UPDRS Part II scores, and gender information from both healthy controls (HC) and those diagnosed with PD was utilized. Employing the Wav2Vec model, a speech-to-text analysis method was implemented on PD patient data. Tasks conducted included word letter classification, word match probability assessment, and analysis of speech waveform characteristics as provided by the model's output.

Results: For the dataset comprising 20 cases, among individuals with PD, the H&Y score averaged 2.50±0.67, the UPDRS II-part 5 score averaged 0.70±1.00, and the UPDRS III-part 18 score averaged 0.80±0.98. Additionally, the number of words derived from decoded text subsequent to speech recognition was evaluated, resulting in mean values of 299.10±16.79 and 259.80±93.39 for the HC and PD groups, respectively. Furthermore, the calculated degree of agreement for all syllables was based on the speech process. The accuracy for the reading sentences was observed to be 0.31 and 0.10, respectively.

Conclusion: This study aimed to demonstrate the effectiveness of wave-to-vec in enhancing speech-to-text analysis for patients with speech disorders. The findings could pave the way for the development of clinical tools for improved diagnosis, evaluation, and communication support for this population.