论文标题
单词错误率估计没有ASR输出：E-WER2
Word Error Rate Estimation Without ASR Output: e-WER2
论文作者
论文摘要
测量自动语音识别（ASR）系统的性能需要手动转录数据才能计算单词错误率（WER），这通常很耗时且昂贵。在本文中，我们继续努力使用声学，词汇和音调特征来估算WER。我们的新方法估算WER使用多端端到端体系结构。我们报告了使用内部语音解码器功能（玻璃框），无语音解码器功能（黑色框）的系统以及无访问ASR系统（NO-box）的系统的结果。 NO-box系统从音素识别结果以及MFCC声学特征中学习联合声学表示，以估计WER。考虑到每个句子，我们的NO-box系统与参考评估的相关性为0.56，在1,400个句子中达到了0.24根平方误（RMSE）。在三个小时的测试集中，E-WER2的总体WER为30.9％，而使用参考转录计算的WER为28.5％。
Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we continue our effort in estimating WER using acoustic, lexical and phonotactic features. Our novel approach to estimate the WER uses a multistream end-to-end architecture. We report results for systems using internal speech decoder features (glass-box), systems without speech decoder features (black-box), and for systems without having access to the ASR system (no-box). The no-box system learns joint acoustic-lexical representation from phoneme recognition results along with MFCC acoustic features to estimate WER. Considering WER per sentence, our no-box system achieves 0.56 Pearson correlation with the reference evaluation and 0.24 root mean square error (RMSE) across 1,400 sentences. The estimated overall WER by e-WER2 is 30.9% for a three hours test set, while the WER computed using the reference transcriptions was 28.5%.
