AUDIO SPECTROGRAM TRANSFORMER (AST): ADVANTAGES OVER TRADITIONAL ALGORITHMS IN SPEECH-TO-TEXT (STT) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
Automatic Speech Recognition (ASR) has seen significant advancements in recent years, largely due to the development of deep learning models. One of the most notable advancements is the Spectrogram Transformer, a variant of the Transformer architecture tailored for audio processing tasks. In this paper, we review the Spectrogram Transformer and compare it with other traditional ASR algorithms. We discuss its benefits, such as improved performance on noisy audio and better modeling of long-range dependencies. Additionally, we explore its applications in various domains, including voice assistants, transcription services, and audio indexing. Through experiments on benchmark datasets like LibriSpeech and the Speech Commands Dataset, we demonstrate the effectiveness of the Spectrogram Transformer in achieving state-of-the-art performance. Our findings suggest that the Spectrogram Transformer offers a promising direction for future advancements in ASR technology.
Keywords: Audio Spectrogram Transformer; Deep Learning; Natural Language Processing (NLP); Speech Processing; Speech Recognition; Transformer Models