Large scale evaluation of importance maps in automatic speech recognition
Large scale evaluation of importance maps in automatic speech recognition
复制标题
DOI：
10.21437/interspeech.2020-2883
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
V. Trinh;Michael I. Mandel
中科院分区：
文献类型：
--
作者：
V. Trinh;Michael I. Mandel
在本文中，我们提出了一个度量，我们称之为结构显着性基准（SSBM），以评估重要性的自动语音识别器计算的个人话语的地图。这些图指示对于正确识别目标词最重要的话语的时频点。我们的评估技术不仅适用于标准分类任务，而且适用于结构化预测任务，如序列到序列模型。此外，我们使用这种方法来执行一个大规模的比较的重要性地图创建我们以前介绍的技术使用“气泡噪声”，以确定重要的点，通过相关的基线方法的基础上平滑的语音能量和强制对齐。我们的研究结果表明，气泡分析方法是更好地识别重要的语音区域比这个基线上的100句AMI语料库。
In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utterances. These maps indicate time-frequency points of the utterance that are most important for correct recognition of a target word. Our evaluation technique is not only suitable for standard classification tasks, but is also appropriate for structured prediction tasks like sequence-to-sequence models. Additionally, we use this approach to perform a large scale comparison of the importance maps created by our previously introduced technique using "bubble noise" to identify important points through correlation with a baseline approach based on smoothed speech energy and forced alignment. Our results show that the bubble analysis approach is better at identifying important speech regions than this baseline on 100 sentences from the AMI corpus.