标题： 计算成本分摊变换器用于流式自动语音识别 显示英文标题

标题： Compute Cost Amortized Transformer for Streaming ASR

摘要： 我们提出了一种流式传输、基于Transformer的端到端自动语音识别（ASR）架构，该架构通过计算成本分摊实现了高效的神经推理。 我们的架构在推理时动态创建稀疏计算路径，从而在整个解码过程中选择性地使用计算资源，实现了计算量的显著减少，同时对准确性影响最小。 该完全可微的架构通过一个配套的轻量级仲裁机制进行端到端训练，在帧级别操作以对每个输入做出动态决策，同时使用可调损失函数来将整体计算水平与预测性能进行正则化。 我们报告了在LibriSpeech数据上进行的计算分摊Transformer-Transducer（T-T）模型实验的实证结果。 我们最佳的模型可以在仅增加3%相对词错误率（WER）的情况下实现60%的计算成本降低。 显示英文摘要

摘要： We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization. Our architecture creates sparse computation pathways dynamically at inference time, resulting in selective use of compute resources throughout decoding, enabling significant reductions in compute with minimal impact on accuracy. The fully differentiable architecture is trained end-to-end with an accompanying lightweight arbitrator mechanism operating at the frame-level to make dynamic decisions on each input while a tunable loss function is used to regularize the overall level of compute against predictive performance. We report empirical results from experiments using the compute amortized Transformer-Transducer (T-T) model conducted on LibriSpeech data. Our best model can achieve a 60% compute cost reduction with only a 3% relative word error rate (WER) increase.