SPEECH RECOGNITION SYSTEM

The Speech recognition systems have become an essential component of modern human–computer interaction, enabling users to communicate with digital devices through natural language. This paper presents the design and development of an intelligent Speech Recognition System that converts spoken language into accurate text in real time. The proposed system utilizes advanced machine learning and deep learning techniques to process audio signals, extract relevant features, and generate meaningful textual output.

The system incorporates audio preprocessing methods such as noise reduction, signal normalization, and feature extraction using techniques like Mel-Frequency Cepstral Coefficients (MFCC). A trained neural network model is employed to improve recognition accuracy and reduce transcription errors. The application is designed to function efficiently in real-time environments, ensuring fast response and reliable performance even with variations in speech patterns, accents, and background noise.Experimental evaluation demonstrates that the system achieves high accuracy and responsiveness, making it suitable for practical applications such as virtual assistants, accessibility tools for differently-abled individuals, automated transcription services, and voice-controlled systems. The results highlight the effectiveness of deep learning approaches in improving speech recognition performance compared to traditional methods.

Keywords: Speech Recognition, Automatic Speech Recognition (ASR), Speech-to-Text Conversion, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing (NLP), Audio Signal Processing, MFCC, Real-Time Transcription.

 

Speech Recognition Automatic Speech Recognition (ASR) Speech-to-Text Conversion Machine Learning Deep Learning Neural Networks Natural Language Processing (NLP) Audio Signal Processing MFCC Real-Time Transcription.