End-to-End ASR Conformers: Revolutionizing Hearing-to-Speech-to-Writing Language Processing Frameworks

This paper introduces a novel end-to-end framework leveraging Conformer architectures to unify the traditionally fragmented pipeline of hearing-to-speech-to-writing language processing. Unlike conventional automatic speech recognition (ASR) systems that cascade separate acoustic, phonetic, and linguistic models prone to cascading errors our approach employs stacked Conformer encoders, which integrate convolution-augmented transformers to capture both local spectral nuances and long-range contextual dependencies in raw audio inputs. The model processes mel-spectrograms directly into intermediate speech representations and final textual outputs via a joint CTC/attention decoder, enabling seamless transformation across modalities without handcrafted features or intermediate alignments. Trained on massive semi supervised datasets exceeding 500,000 hours, the framework achieves state-of-the-art word error rates (WER) of 1.9% on LibriSpeech clean test sets and 4.2% on noisy subsets, outperforming prior transformer and RNN-T baselines by 20-30% relatively. Streaming variants maintain real-time factors below 0.2 on edge devices, supporting applications in live captioning, hearing aids, and neural prosthetics. Ablation studies validate the Conformer sandwich structure's role in modelling prosody and disfluencies, while extensions incorporate multimodal embeddings for brain-signal decoding. This work paves the way for holistic, human-like speech-to-text systems that bridge auditory perception with linguistic expression, addressing real-world challenges in noisy, multilingual, and spontaneous speech scenarios.