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Patients with Parkinson's disease (PD) may have difficulties in speaking because of reduced coordination of the muscles that control breathing, phonation, articulation, and prosody. Symptoms that may occur are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, the speed of speech, uncontrolled repetition of words, and difficult speech intelligibility. To date, evaluation of the speech intelligibility is performed based on the unified PD rating scale. Specifically, section 3.1 (eloquence) of the cited scale provides the specialist with some tips to evaluate the patient's speech ability. With the aim of evaluating the speech intelligibility by measuring the variation in parameters in an objective manner, we show that a speech-to-text (STT) system could help specialists to obtain an accurate and objective measure of speech, phrase, and word intelligibility in PD. STT systems are based on methodologies and technologies that enable the recognition and translation of spoken language into text by computers and computerized devices. We decided to base our study on Google STT conversion. We expand Voxtester, a software system for digital assessment of voice and speech changes in PD, in order to perform this study. No previous studies have been presented to address the mentioned challenges based on STT. The experiments here presented are related with detection/classification between pathological speech from patients with PD and regular speech from healthy control group. The results are very interesting and are an important step toward assessing the intelligibility of the speech of PD patients.
Dimauro et al. (2017) studied this question.