Introduction

The complete understanding of the decision-making process of Convolutional Neural Networks (CNNs) is far from being fully reached. Many researchers proposed techniques to interpret what a network actually “learns” from data. Nevertheless, many questions still remain unanswered. We study one aspect of this problem by reconstructing speech from the intermediate embeddings computed by a CNNs. Specifically, weconsider a pre-trained network that acts as a feature extractor from speech audio. We investigate the possibility of inverting these features, reconstructing the input signals in a black-box scenario, and quantitatively measure the reconstruction quality by measuring the word-error-rate of an off-the-shelf ASR model. Experiments performed using two different CNN architectures trained for six different classification tasks, show that it is possible to reconstruct time-domain speech signals that preserve the semantic content, whenever the embeddings are extracted before the fully connected layers.

Method

Support or Contact

For any information regarding the paper or the code send us an email at luca.comanducci@polimi.it!