Ai-Based Speech Recognition Systems For Assistive Technologies

Abstract

This study introduces an Automatic Speech Recognition (ASR) system that utilizes artificial intelligence (AI) to enhance assistive technologies for individuals who experience difficulties with speech and communication.  The system uses Transformer topologies in conjunction with Recurrent Neural Networks (RNNs) to extract strong speech features from raw audio data using sophisticated data pretreatment methods like spectrogram transformation and noise reduction.  To make sure it can adjust to different accents and vocabulary, training uses big datasets like Libri Speech, which are then fine-tuned using domain-specific data.  With the help of sequence-to-sequence loss functions and Connectionist Temporal Classification (CTC), the model achieves peak performance.  Post-processing ensures grammatical correctness after real-time inference.  The evaluation results show that the model outperforms baseline ASR models in terms of accuracy, precision, recall, and response times.  These outcomes prove that the system has what it takes to be an effective and trustworthy resource for assistive speech recognition.