Towards the development of a non-intrusive objective quality measure for DNN-enhanced speech

摘要

Recently, several works have focused on leveraging the advances of deep neural networks (DNN) to a variety of domains, including speech enhancement. While advances in instrumental quality metrics have been made, particularly for enhanced speech, there is still relatively little research assessing how useful such metrics are for DNN-enhanced speech. This work aims to fill this gap. We performed online listening tests using the outputs of three different DNN-based speech enhancement models for both denoising and dereverberation. When assessing the predictive power of several objective metrics, we found that existing non-intrusive methods fail at monitoring signal quality. To overcome this limitation, we propose a new metric based on a combination of a handful of relevant acoustic features. Results inline with those obtained with intrusive measures are then attained. In a leave-one-model-out test, the proposed non-intrusive metric is also shown to outperform two non-intrusive benchmarks for all three DNN enhancement methods, showing the proposed method is capable of generalizing to unseen models.