Deep neural networks (DNN) are able to successfully process and classify speech utterances. However, understanding the reason behind a classification by DNN is difficult. One such debugging method used with image classification DNNs is activation maximization, which generates example-images that are classified as one of the classes. In this work, we evaluate applicability of this method to speech utterance classifiers as the means to understanding what DNN "listens to". We trained a classifier using the speech command corpus and then use activation maximization to pull samples from the trained model. Then we synthesize audio from features using WaveNet vocoder for subjective analysis. We measure the quality of generated samples by objective measurements and crowd-sourced human evaluations. Results show that when combined with the prior of natural speech, activation maximization can be used to generate examples of different classes. Based on these results, activation maximization can be used to start opening up the DNN black-box in speech tasks.
翻译:深神经网络 (DNN) 能够成功处理和分类语音表达语句。 但是, 理解 DNN 分类背后的原因非常困难。 在图像分类 DNN 中使用的一种调试方法就是激活最大化, 生成被归类为类之一的示例图像。 在这项工作中, 我们评估该方法对语音表达分类器的适用性, 作为理解 DNN “ 列表” 的手段 。 我们用语音命令程序培训了一名分类员, 然后用激活最大化来从经过培训的模型中提取样本。 然后, 我们用WaveNet vocoder 组合成音频, 进行主观分析。 我们通过客观测量和众源人类评估来测量生成的样本的质量。 结果显示, 当与先前的自然演讲相结合时, 激活最大化可以用来生成不同类的示例。 基于这些结果, 激活最大化可以用来启动语音任务的 DNN Box 。