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The Transformer architecture has proven to be highly effective for AutomaticSpeech Recognition (ASR) tasks, becoming a foundational component for aplethora of research in the domain. Historically, many approaches have leanedon fixed-length attention windows, which becomes problematic for varied speechsamples in duration and complexity, leading to data over-smoothing and neglectof essential long-term connectivity. Addressing this limitation, we introduceEcho-MSA, a nimble module equipped with a variable-length attention mechanismthat accommodates a range of speech sample complexities and durations. Thismodule offers the flexibility to extract speech features across variousgranularities, spanning from frames and phonemes to words and discourse. Theproposed design captures the variable length feature of speech and addressesthe limitations of fixed-length attention. Our evaluation leverages a parallelattention architecture complemented by a dynamic gating mechanism thatamalgamates traditional attention with the Echo-MSA module output. Empiricalevidence from our study reveals that integrating Echo-MSA into the primarymodel's training regime significantly enhances the word error rate (WER)performance, all while preserving the intrinsic stability of the originalmodel.

Echotune: A Modular Extractor Leveraging the Variable-Length Nature of Speech in ASR Tasks