对“自动语音识别的分段最小贝叶斯风险解码”的修正

V. Goel, Shankar Kumar, W. Byrne
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引用次数: 1

摘要

本文的目的是对我们最近发表的论文[1]中的实验结果进行修正和扩展。在[1,第III-B节]中,我们提出了一种基于风险的格切割(RLC)方法,将ASR词格分割成更小的子格序列。该程序的目的是对原始格进行重构,以提高最小贝叶斯风险(MBR)和其他格重记程序的效率。考虑到分割后的网格要被重新分割,在分割过程中不能丢失来自原始网格的路径是至关重要的。在我们最初发表的实验报告中,一些原始路径被无意地从分割的网格中丢弃。这影响了MBR的性能。在本文中,我们简要回顾了分割算法,并解释了我们之前实验中的缺陷。我们发现在修正后的程序下,单词错误率(WER)有了一致的小幅改善。更重要的是,我们报告的实验证实了晶格分割过程确实保留了原始晶格中的所有路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Corrections to "Segmental minimum Bayes-risk decoding for automatic speech recognition"
The purpose of this paper is to correct and expand upon the experimental results presented in our recently published paper [1]. In [1, Sec. III-B], we present a risk-based lattice cutting (RLC) procedure to segment ASR word lattices into sequences of smaller sublattices. The purpose of this procedure is to restructure the original lattice to improve the efficiency of minimum Bayes-risk (MBR) and other lattice rescoring procedures. Given that the segmented lattices are to be rescored, it is crucial that no paths from the original lattice be lost in the segmentation process. In the experiments reported in our original publication, some of the original paths were inadvertently discarded from the segmented lattices. This affected the performance of the MBR results presented. In this paper, we briefly review the segmentation algorithm and explain the flaw in our previous experiments. We find consistent minor improvements in word error rate (WER) under the corrected procedure. More importantly, we report experiments confirming that the lattice segmentation procedure does indeed preserve all the paths in the original lattice.
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