# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 2 State of the Art
### Clinical Application of Deep Learning Through the Analysis of Dysarthria  
There is a compelling opportunity for research that utilizes deep learning methodologies and phonation analysis techniques in the study of neurological diseases (Parkinson's and Alzheimer's) and neurodevelopmental syndromes (SMS). A recent comprehensive review of the literature highlighted a scarcity of such research. Voice analysis tools aid in characterizing phonation in neurological diseases like Parkinson's, where laryngeal tremors occur in 60% of cases [54]. Fluctuations in frequencies between 6 and 10 Hz signify neurodegenerative activity, along with symptoms like excessive vocal muscle tension and changes in fundamental frequency (60) [55; 56; 1]. Studies on dysarthria primarily focus on speech articulation and unintelligibility levels, utilizing acoustic indices derived from the first and second formants (F1 and F2) [57; 58; 59; 60; 61]. Research in this field has been extensive, with studies by [PERSON] and [PERSON] [62; 63] analyzing phonation characteristics in Parkinson's patients. Additionally, [PERSON] and [PERSON] [64; 65], along with [PERSON] et al. [66], focused on voice recordings using machine learning techniques. [PERSON] et al. [67] found voice tasks more effective in detecting [PERSON] than speech tasks. In the realm of deep learning models, [PERSON] et al. [18], [PERSON] et al. [15], and [PERSON] et al. [17] proposed various neural-network-based approaches. [PERSON] et al. [68] utilized datasets like the Parkinson's Telemonitoring Voice Data Set, while [PERSON] et al. [16] are developing convolutional neural network models using mobile phone-collected voice signals. While Parkinson's remains a primary focus, there is emerging interest in Alzheimer's disease [69; 70; 71; 72], though fewer studies have explored phonation data in this context [9; 73; 74; 75; 76]. Due to the specificity of SMS, there is a paucity of research investigating the application of deep learning techniques to vocal analysis within this population. Nevertheless, studies exploring the intersection of deep learning and speech disorders, particularly dysarthria, do not exist. Consequently, this literature review incorporates research on deep learning approaches to dysarthria, given its relevance to the broader domain of speech disorders. In [77], the authors explored a promising approach to enhance dysarthric speech recognition by augmenting the dataset with healthy speech samples. This technique could lead to improve communication tools for individuals with dysarthria. In [78], the authors discussed the challenges of recognizing disordered speech using automatic speech recognition (ASR) technologies using the UASpeech database. Despite the rapid progress in ASR for normal speech, disordered speech remains a highly challenging task. Shahamiri represents an innovative approach that combines deep learning and visual features to improve dysarthric speech recognition accuracy, benefiting patients with dysarthia [79]. The authors proposed an innovative approach that converts voice signals into visual rep resentations called voicegrams. [PERSON] et al. 's study endeavored insights into using deep learning techniques and acoustic features to automatically classify dysarthria severity levels. This research work contributed to improving communication tools and therapies for individuals with dysarthria [80, 81]. The former explored the automatic classification of dysarthria severity using three deep learning approaches: DNN, CNN, and LSTM with the UA-Speech and TORGO databases. The latter compared various deep learning models for classifying dysarthria severity levels using disorder-specific acoustic features using MFCCs combined with i-vectors in a DNN classifier.