Connectionist Temporal Classification Based Speechto-Text Model with Optimized Preprocessing Techniques

Evolution in Speech-to-Text (STT) systems becomes inevitable in filling the gap between human communication and machine comprehension for various applications, such as customer support, real-time documentation, and accessibility. This work presents a strong STT model, which benefits from powerful preprocessing and data augmentation that capitalizes on Connectionist Temporal Classification (CTC) loss. An ensemble strategy to improve performance and adaptability is essential for using the Mel Frequency Cepstral Coefficients (MFCCs) and SpecAugment techniques for data diversity. Extensive experiments also show exceptional performance with a high average overall accuracy of 95.8%; specific applications in healthcare or legal transcription (96.3%), real-time streams of 95.8%, and noisy environments of 94.5%. The system demonstrated a low error rate; the character error rate was 2.1%, and the word error rate was calculated to be 4.2%. The model shows precision and reliability, which would establish new benchmarks in diverse real-world scenarios.