用帧级准则压缩语音识别模型，内存降低且精度不降
Lightweight Transducer Based on Frame-Level Criterion
- 用CTC对齐结果为每帧定标签，避免全矩阵计算
- 在AISHELL-1上达到与原transducer相当的识别率
- 分离空白/非空白概率，提升训练稳定性
基于序列级准则的转导器模型因生成大型概率矩阵而占用大量内存。本文提出一种基于帧级准则的轻量级转导器模型，利用CTC强制对齐算法确定每帧标签，使编码器输出与解码器输出在对应时间点直接结合，而非像传统转导器那样逐元素相加，显著降低内存和计算开销。为解决标签中空白过多导致的分类不平衡问题，将空白与非空白概率解耦，并截断空白分类器的梯度回传至主网络。在AISHELL-1数据集上的实验表明，该方法使轻量级转导器达到与标准转导器相近的性能；此外，通过引入更丰富的信息预测空白概率，其表现优于标准转导器。
原文摘要 · Abstract (English)
The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix. We proposed a lightweight transducer model based on frame-level criterion, which uses the results of the CTC forced alignment algorithm to determine the label for each frame. Then the encoder output can be combined with the decoder output at the corresponding time, rather than adding each element output by the encoder to each element output by the decoder as in the transducer. This significantly reduces memory and computation requirements. To address the problem of imbalanced classification caused by excessive blanks in the label, we decouple the blank and non-blank probabilities and truncate the gradient of the blank classifier to the main network. Experiments on the AISHELL-1 demonstrate that this enables the lightweight transducer to achieve similar results to transducer. Additionally, we use richer information to predict the probability of blank, achieving superior results to transducer.
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