# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 1 Introduction
In our previous study [56], a novel classification protocol that deviates from the conventional process primarily in terms of signal segmentation was introduced. Syllabic-level feature variances were used instead of features themselves for training classifiers. The data used in that study consisted of three different word utterances repeated multiple times at various places in the paragraphs read by 40 different speakers. Research on connected speech and sustained phonations has shown different strengths individually in capturing the effects of PD. In this study, a novel framework that draws on and combines these strengths of connected speech was developed and evaluated. The development and evaluation of this framework is a step toward establishing an efficient process that can be used to aid in diagnosis and telemonitoring applications. The proposed methodology closely follows the procedure adopted in our earlier study [56], with significant modifications. Here, different options for methodological steps representing the conventional and proposed frameworks were identified. The optimal choice for each step was identified through a comprehensive analysis. Features were extracted only from all the voiced segments in paragraph recordings collected from people with PD and HCs. A variety of classifiers were trained on the features themselves using hold-out validation. Through this approach, pitch-synchronous (PS) segmentation was proven to capture vocalic dynamics more efficiently, resulting in better and more reliable PD classification. To test the efficiency of the proposed framework, two separate datasets consisting of paragraph recordings in different languages were used, with each dataset being utilized in the training and testing phases individually. The features from our earlier study [56] were divided into two groups: mel-frequency cepstral coefficients (MFCCs) and pitch-synchronous features (PSFs), and they were compared. MFCCs have been widely used for speech recognition and speaker identification tasks. They have also been part of many studies aimed at PD classification and stood out as some of the best features yielding good performance [32; 34; 35; 37; 57]. The methodology adopted to evaluate the MFCCs for PD classification here has been designed to replicate these research works, where the results show a classification accuracy close to 90%. Most of these studies employ linear SVM kernels for classification, and we included the same in our classifier set. PSFs, designed to work with PS segmentation of voiced speech, had superior performance among the two feature groups. In summary, the benefits of this study are as follows:  
1. Efforts to establish an analytical framework that can be automated to classify between PD and HC using vocalic dynamics. 2. Providing evidence as to the robustness of the framework for the language being spoken. 3. Evaluation of PSFs for PD classification. 4 Providing evidence for the shortcomings of using MFCCs for PD classification due to their inherent nature of embedding patient identifiable information.