## 2 Literature Review
### Voice Analysis for Emotion Detection in Neurodegenerative Diseases  
In voice analysis for emotion detection, speech patterns and acoustic properties are employed to identify emotional states. This approach is becoming more widely used in healthcare, especially in the treatment of neurodegenerative illnesses like PD, AD, and MS, where mental wellness is essential to general health [10; 45]. Emotion detection modeling is the process of creating voice analysis-based models that can differentiate between various emotional states. **Voice analysis for emotion detection** in neurodegenerative diseases is specifically used for the following:  
* _Monitoring emotional health:_ AD, PD, and MS patients should have their emotional health regularly assessed because it has a significant impact on their overall quality of life [46];
* _Early detection of emotional changes_: Recognizing the early warning indicators of emotional disorders, such as anxiety and depression, which are widespread in neurodegenerative illnesses;
* _Personalized interventions_: Delivering prompt, individualized emotional support in response to discerned shifts in emotional states. Emotional states, whether positive or negative, induce significant influence on various physiological aspects of the human body, such as changes in muscle tension, blood pressure, heart rate, respiration, and vocal tone [47]. These physiological changes are reflected in vocal characteristics, independent of the lexical content of speech. Recent research in vocal analysis [48; 49] has demonstrated that different emotional states can be differentiated based on specific acoustic features, including temporal and spectral features. Speech rate, pitch, tone, and rhythm analysis are used to discern emotional states. Natural language processing (NLP) and ML methods are utilized to extract emotional indicators from speech data [50]. The methods used for performing voice analysis for emotion detection are based on ML and deep learning methods. Various ML algorithms, including support vector machines, K-nearest neighbors, and decision trees, have been implemented to detect emotions from audio data. Features, such as Mel frequency cepstral coefficients (MFCCs), are used to achieve high accuracy in emotion classification [51]. Deep neural networks using convolutional and pooling layers have shown results with good accuracy. Emotions like melancholy, happiness, and rage are categorized by processing raw audio data and extracting attributes [52]. Voice analysis for emotion detection is a promising field with significant potential for improving the management of neurodegenerative diseases, such as PD and AD. Recent trends include advancements in acoustic feature extraction for capturing nuances in voice signals, ML techniques (CNNs and RNNs), multimodal emotion detection (combining voice analysis with facial expression recognition, physiological signals, and body language analysis), and real-time processing. These developments enable continuous and accurate monitoring of emotional states, which is crucial for personalized healthcare interventions. However, challenges such as data quality (ambient noise, speech variability due to accent, dialect, etc. ), model generalizability across different populations, privacy concerns about recorded voice data, technical limitations (low-latency and high-accuracy processing requirements), and the need for clinical validation by trials and studies must be addressed to fully realize the benefits of this technology in proactive healthcare. The authors of [53] perform an experiment comparing different machine learning techniques on the same datasets using the same features. For example, they compare using only time domain features, only frequency domain features, or both, using multiple machine learning approaches: random forests (RFs), naive Bayes, support vector machines (SVMs), and CNNs, among others. In their experiments, the CNN approach significantly outperformed the other models.