# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 1 Introduction
The choice of speech stimuli and the specific features extracted from them depend on the research question and the type of speech impairment (phonatory or articulatory) targeted for classification. Three types of speech tasks are commonly employed to elicit the effects of PD. These tasks include sustained vowel phonations, diadochokinetic (DDK; repetition of syllables), and connected speech (word/sentence/paragraph readings, monologues, etc.) tasks. Studies using sustained vowel phonations [35; 32; 36; 37] focus on regularities or irregularities in phonation to train efficient classification models. Sustained phonations are relatively easy to analyze and do not suffer from language or accent barriers other than connected speech. However, researchers have reported the higher suitability of connected speech over sustained phonations to study pathological speech [38; 39] due to their closeness to everyday speech. Although the DDK task is speech-like, it is less natural, and research consensus is mixed on the effectiveness of DDK in PD classification. Some studies have shown no significant differences between PD and HC groups [40], and other more recent studies have shown that people with PD have lower DDK rates than healthy speakers [41]. The individual diversities in cognitive processing behind speech production reflect misread texts, variations in pause durations, and other such manifestations. These manifestations make connected speech more complicated for analysis because it often requires manual corrections to maintain consistency in the data, which is essential for unbiased classification experiments. Hence, fewer research studies like [28] aim to automate PD classification/monitoring using connected speech. [PERSON] et al. showed significant progress in utilizing connected speech for automatic PD classification [42; 43]. They used multiple types of speech tasks and observed that features extracted from monologue (connected speech) were sensitive enough and tasks like sustained phonations and DDK were not optimal to capture impairments due to prodormal PD. Studies like [44; 45; 46] used passage reading tasks to evaluate continuous speech and observe the variability between PD and HC using temporal and spectral features. [PERSON] et al. focused on specific vowel sounds and observed reductions in vowel space in PD speech [47]. In another study [48], various speech features, including NHR, fundamental frequency, relative shimmer, and jitters, were evaluated and compared. Lower values for these features are considered desirable for good speech signal quality. Additionally, the study explored the variants of vowel space area (triangular VSA--tVSA and quadrilateral VSA--qVSA), and FCR (Formant Centralization Ratio), which are indicators of potential dysarthria-related conditions that could lead to compression in VSA. Furthermore, some recent studies [49] have discussed the application of different automatic speech recognition (ASR) services (such as Amazon, Google, and IBM) to differentiate between healthy controls and PD patients. Nonlinear mixed effects models (nLMEM) accounted for the unequal variance between healthy controls and individuals with speech disorders. In recent years, there has been a notable shift towards utilizing deep learning models for speech processing in PD classification. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [50; 51; 52; 53] have gained prominence in detecting and classifying PD based on speech signals. These models have the capability to automatically extract features from speech signals [54; 55] and learn to classify them based on underlying patterns within the data. However, it is essential to acknowledge that the use of deep learning techniques often has limitations that include data dependency, computationally intensive, lack of interpretability, and high training time. The positive aspect is that these opportunities provide us with a range of future prospects.