THE BAYESIAN NEURAL NETWORKS FOR SPEECH RECOGNITION

Abstract

Speech recognition has become a critical component of modern air applications, offering new ways for humans to interact with machines. With advances in learning and neural networks, speech recognition continues to improve, expanding its capabilities and enhancing user experiences across various industries .Phoneme classification represents a significant challenge in automatic speech recognition (ASR) systems, primarily due to variability in speech data influenced by factors such as speaker differences, background noise, and recording conditions. Bayesian Neural Networks (BNNs) provide a robust framework for addressing uncertainty in model predictions, enhancing performance by incorporating prior knowledge and capturing the uncertainty inherent in the data. This paper presents a detailed exploration of Bayesian Neural Networks (BNNs) for phoneme classification using the TIMIT database. We demonstrate the effectiveness of BNNs in improving classification accuracy and model robustness through extensive experiments and comparisons with traditional neural networks.