提出分块对齐器，提升流式语音识别训练与推理效率。
Chunkwise Aligners for Streaming Speech Recognition
- 将音频分块，每标签对齐至所在块首帧，块间通过概率衔接。
- 在离线与流式场景下准确率媲美传统转换器模型。
- 训练和解码效率显著优于现有方法，适合实时应用。
我们提出一种新型流式自动语音识别（ASR）架构——分块对齐器（Chunkwise Aligner）。尽管转换器（Transducer）是当前流式ASR的标准模型，但其训练成本高，因需计算所有可能的音频-标签对齐。近期提出的对齐器虽降低了训练开销，但因舍弃显式对齐而无法用于流式场景。本方法通过将音频划分为若干块，将每个标签对齐到所属块的最左帧，块间的过渡由学习得到的块结束概率管理。实验表明，分块对齐器在离线与流式两种场景下的准确率均达到与转换器相当的水平，同时在训练与解码效率方面表现更优。
原文摘要 · Abstract (English)
We propose the Chunkwise Aligner, a novel architecture for streaming automatic speech recognition (ASR). While the Transducer is the standard model for streaming ASR, its training is costly due to the need to compute all possible audio-label alignments. The recently introduced Aligner reduces this cost by discarding explicit alignments, but this modification makes it unsuitable for streaming. Our approach overcomes this limitation by dividing the audio into chunks and aligning each label to the leftmost frames of its chunk, whereas transitions between chunks are managed by a learned end-of-chunk probability. Experiments show that the Chunkwise Aligner not only matches the Transducer's accuracy in both offline and streaming scenarios, but also offers superior training and decoding efficiencies.
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