Focusing on the challenges of stammering, this study highlights its impact on social and professional opportunities for individuals. Utilizing the Fluency Bank dataset, it proposes an innovative algorithm for stammering correction that integrates Enhanced One-dimensional Local Binary Patterns for feature extraction and an Adapted Multi-Layer Perceptron for Regression as a generative classifier. The algorithm aims to create a choral effect by delivering anti-stammered speech to the user, potentially leading to improved fluency in speech and offering new hope for those affected by stuttering.
