The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models
continues to improve as they are scaled to larger sizes, with some now reaching
billions of parameters. Widespread deployment and adoption of these models,
however, requires computationally efficient strategies for decoding. In the
present work, we study one such strategy: applying multiple frame reduction
layers in the encoder to compress encoder outputs into a small number of output
frames. While similar techniques have been investigated in previous work, we
achieve dramatically more reduction than has previously been demonstrated
through the use of multiple funnel reduction layers. Through ablations, we
study the impact of various architectural choices in the encoder to identify
the most effective strategies. We demonstrate that we can generate one encoder
output frame for every 2.56 sec of input speech, without significantly
affecting word error rate on a large-scale voice search task, while improving
encoder and decoder latencies by 48% and 92% respectively, relative to a strong
but computationally expensive baseline.

通过在编码器中应用多个帧压缩层，我们能够以较低的计算复杂度实现在每 2.56 秒的输入语音中生成一个编码器输出帧，而又不显著影响大规模语音搜索任务的词错误率，并相比于强大但计算代价高昂的基准模型，降低编码器和解码器延迟分别达到了 48% 和 92%。