The Transformer architecture has proven to be highly effective for Automatic Speech Recognition (ASR) tasks, becoming a foundational component for a plethora of research in the domain. Historically, many approaches have leaned on fixed-length attention windows, which becomes problematic for varied speech samples in duration and complexity, leading to data over-smoothing and neglect of essential long-term connectivity. Addressing this limitation, we introduce Echo-MSA, a nimble module equipped with a variable-length attention mechanism that accommodates a range of speech sample complexities and durations. This module offers the flexibility to extract speech features across various granularities, spanning from frames and phonemes to words and discourse. The proposed design captures the variable length feature of speech and addresses the limitations of fixed-length attention. Our evaluation leverages a parallel attention architecture complemented by a dynamic gating mechanism that amalgamates traditional attention with the Echo-MSA module output. Empirical evidence from our study reveals that integrating Echo-MSA into the primary model's training regime significantly enhances the word error rate (WER) performance, all while preserving the intrinsic stability of the original model.

翻译：Transformer架构已被证明在自动语音识别（ASR）任务中具有高效性，成为该领域大量研究的基础组件。以往诸多方法多依赖于固定长度注意力窗口，这在处理时长和复杂度各异的语音样本时会引发问题，导致数据过度平滑并忽略重要的长期依赖关系。为解决这一局限性，我们提出了Echo-MSA，一种配备可变长度注意力机制的轻量模块，能够适应不同复杂度和时长的语音样本。该模块可灵活提取从帧、音素到单词及语篇等多粒度的语音特征。所提出的设计捕捉了语音的变长特性，克服了固定长度注意力的局限。我们采用并行注意力架构，结合动态门控机制，将传统注意力与Echo-MSA模块输出相融合。实验证据表明，在主模型训练过程中集成Echo-MSA可显著提升词错误率（WER）性能，同时保持原模型的内在稳定性。