1 research outputs found
Towards Debugging Deep Neural Networks by Generating Speech Utterances
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.Comment: Accepted to Interspeech 201