Enhancing Automatic Speech Recognition Through Integrated Noise Detection Architecture

Modern automatic speech recognition systems have achieved remarkable performance through deep learning architectures, particularly models based on self-supervised learning paradigms. However, real-world deployment scenarios frequently involve challenging acoustic environments where background disturbances significantly compromise recognition accuracy. When processing audio containing substantial non-speech content, conventional systems often generate incoherent outputs, leading to elevated error rates that undermine practical utility. The fundamental challenge addressed in this work stems from the inability of standard ASR architectures to explicitly differentiate between meaningful speech signals and irrelevant acoustic interference. This limitation manifests as increased word error rates and character error rates when processing audio with poor signal-to-noise characteristics. This paper introduces an augmented architecture that extends the wav2vec2 model by incorporating a parallel noise detection pathway. Unlike conventional approaches that handle noise through preprocessing or post-processing stages, the proposed method integrates noise awareness directly into the feature learning process.