Designing a Smart Device for Speech Stammer Detection Features based on Artificial Neural Network

Stammering is a speech disorder characterized by disruptions in verbal fluency, often affecting an individual’s confidence and social interactions. This project introduces a real-time stammering detection and correction system that utilizes Natural Language Processing (NLP) and Artificial Neural Networks (ANN) to enhance speech fluency. The system functions by capturing live audio input, processing it through NLP techniques to convert speech into text, and applying noise reduction methods to improve clarity. Once the audio is transcribed, an ANN-based algorithm is used to analyze the spoken language, accurately identifying instances of stammering. By leveraging machine learning, the system effectively distinguishes between natural speech patterns and stammered words or syllables, ensuring precise detection. Upon detecting stammered speech, the system reconstructs it into a more fluent form by eliminating unnecessary repetitions and prolongations while preserving the original meaning and intonation of the speaker. The refined speech is then synthesized back into audio, ensuring smooth and natural communication. This real-time process allows individuals with stammering disorders to speak more confidently, as the system provides immediate feedback and correction, significantly reducing speech disruptions. The incorporation of NLP enhances speech understanding, while ANN enables adaptive learning, ensuring continuous improvement in detection accuracy. Additionally, the integration of noise reduction techniques ensures that external disturbances do not interfere with speech processing, making the system more reliable in diverse environments.