International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Design and Implementation of a Framework for Neurological disorder detection system using Speech Analysis using AI

Author(s) Ms. Nukala Bhavana Surya Vali, Dr. V Anuragh, Ms. Chikatla Rohitha
Country India
Abstract Neurological disorders such as Parkinson’s disease and Alzheimer’s disease affect speech production and communication abilities. Variations in speech patterns including articulation, rhythm, pitch, vocal intensity, and fluency can act as early indicators of neurological abnormalities. This paper presents a Machine Learning and Deep Learning-based Speech Analysis System for detecting neurological disorders using speech signals. The proposed system performs preprocessing operations such as noise reduction and normalization on speech recordings. Mel Spectrogram feature extraction is applied to represent speech signals in the time-frequency domain. Multiple machine learning algorithms including Decision Tree, Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) are implemented for classification. Among these models, the LSTM model achieved superior performance due to its ability to capture temporal speech dependencies. The system classifies speech samples into Alzheimer’s, Parkinson’s, and Healthy categories. The implementation is developed using Python, Django Framework, Librosa, TensorFlow/Keras, and Scikit-Learn to provide an interactive web-based platform for automated speech analysis and prediction. Experimental evaluation using Accuracy, Precision, Recall, and F1-Score demonstrates that the proposed system provides reliable and efficient neurological disorder detection.
Keywords Speech Analysis, Neurological Disorder Detection, Mel Spectrogram, LSTM, Parkinson’s Disease, Alzheimer’s Disease, Machine Learning, Deep Learning.
Field Engineering
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-17
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.78714
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E-ISSN 2582-2160
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