Stammering (stuttering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. Stammering can significantly impact the quality of a person's life. Stammerers may affect in refraining from speaks and may waste opportunities to make friends, present own ideas and opinions in public, and negatively influence in job interviews. This study uses a dataset that involves speech signals with stammering, it can be called the Fluency Bank (FB). In this work, an efficient algorithm to perform stammering corrections (anti-stammering) is proposed. This algorithm contains an advanced techniques. These advanced techniques are the Enhanced One-dimensional Local Binary Patterns (EOLBP) and Adapted Multi-Layer Perceptron for Regression (AMLPR).The EOLBP is a useful feature extraction and the AMLPR is a generative classifier. Besides, choral or feedback speech is supplied by using these advanced techniques through feeding the algorithm's output (anti-stammered speech) to the stammerer's ear to provide the choral effect. This choral effect can produce a fluent speech via the stammerer's mouth that can contribute to curing people who stutter.