Clearer Voices, Softer Struggles: Addressing Speech and Pain Challenges in Cerebral Palsy

Abstract

The proposed system is designed to harness the power of machine learning to enhance speech clarity and detect signs of pain, offering a dual-purpose solution tailored to the needs of individuals with cerebral palsy. At its core, the system uses techniques like Mel-Frequency Cepstral Coefficients (MFCC) to capture the essential acoustic characteristics of speech. This information feeds into the K-Nearest Neighbors (KNN) algorithm, which determines whether a speech utterance is intelligible. By providing instant feedback on clarity, the system empowers users to adjust their speaking habits immediately, improving their communication effectiveness in real-time. In addition to improving speech intelligibility, the system employs the Inception V3 deep learning model to identify signs of pain by analyzing facial expressions and subtle, real-time cues that may indicate discomfort or distress. By combining these two functionalities speech clarity enhancement and pain detection the system takes a holistic approach to address communication challenges and pain management simultaneously. This integrated solution aims to not only improve everyday interactions for individuals with cerebral palsy but also provide timely intervention for pain, enhancing their overall well-being through advanced, user-friendly technology.