On the deficiency of intelligibility metrics as proxies for subjective intelligibility.

A recent trend in deep neural network (DNN)-based speech enhancement consists of using intelligibility and quality metrics as loss functions for model training with the aim of achieving high subjective speech intelligibility and perceptual quality in real-life conditions. In this study, we analyze a variety of loss functions, including some based on state-of-the-art intelligibility and quality metrics, to train an end-to-end speech enhancement system based on a fully convolutional neural network. The loss functions include perceptual metric for speech quality evaluation (PMSQE), scale-invariant signal-to-distortion ratio (SI-SDR), SI-SDR integrating speech pre-emphasis, short-time objective intelligibility (STOI), extended STOI (ESTOI), spectro-temporal glimpsing index (STGI), and a composite loss function combining STGI and SI-SDR. While DNNs trained with these loss functions produce notable speech intelligibility (and quality) gains according to pertinent objective metrics, we conduct a subjective intelligibility test that contradicts this result, showing no intelligibility improvement. From the results of this study, our conclusion is twofold: (1) subjective intelligibility evaluation is currently not replaceable by objective intelligibility evaluation, and (2) both the development of meaningful intelligibility metrics and DNN-based speech enhancement systems that can consistently improve the intelligibility of noisy speech for human listening remain open problems.