Virtual Voice Assistance for Partially Dumb People Using Audio Processing with Deep Learning

This paper presents a novel virtual voice assistance system designed specifically for partially dumb individuals, leveraging advanced audio processing and deep learning techniques. Traditional voice assistants struggle to accurately interpret partial or unclear speech, creating significant communication barriers for individuals with speech impairments. To address this, our system employs robust preprocessing methods, including noise reduction and Mel-Frequency Cepstral Coefficients (MFCC) extraction, to process incomplete speech signals. A Recurrent Neural Network (RNN) model, trained on a diverse dataset of partial speech inputs, enables accurate interpretation and contextual understanding of spoken words. The system integrates real-time processing with a text-to- speech (TTS) module, allowing it to generate meaningful responses tailored to the user’s intent. Extensive testing shows the proposed system achieves 91% recognition accuracy and significantly reduces Word Error Rate (WER) compared to baseline methods. Additionally, case studies demonstrate its effectiveness in enhancing communication for users in real-world scenarios.This work contributes to inclusive technology by addressing a critical gap in voice- assisted communication. Future work includes expanding the dataset to accommodate diverse accents and optimizing the model for deployment on mobile and wearable devices, thereby extending its accessibility and impact.