The OCPT Mechanism: An Innovative Methodology to Erect the Language Understanding to Build the Enhanced STT Model

Generative Pretrained Transformers (GPT) predict the sequence of words when building the language model in speech recognition. The traditional model inherits the high range biases during training the data, this issue leads to inappropriate outputs, the proposed research, the Optimized Constructive Pretrained Transformers(OCPT) mechanism emphasizes tuning GPT models specific to domain language modeling, accentuating the value of an organized approach that involves data preprocessing, model initialization, fine-tuning, and assessment. This model helps to build a more efficient and effective Speech-to-text model. The model obtained 97.12% accuracy on the validation set, compared with the AuGPT model which produces 91% accuracy, indicating the proposed model's ability to understand and generate contextually relevant text. During the training phase, the model was hyperparameter-tuned to improve performance metrics including learning rate, batch size, and training epochs. The Adam optimizer was important in accurately adjusting the model parameters to ensure convergence and stability. To avoid overfitting and ensure the model's generalizability, accuracy, and loss have been evaluated throughout the training and validation phases. The findings assessed the model's learning trajectories and performance indicators, demonstrating its efficacy.