Training Supervised Speech Separation System to Improve STOI and PESQ Directly

Supervised speech separation methods train learning machine to cast the noisy speech to the target clean speech. Most of them use mean-square error (MSE) as loss function. However, MSE is not the perfect choice because it doesn't match the human auditory perception. Short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) are closely related to the human auditory perception and widely used in speech separation research as evaluation criteria. Therefore, STOI and PESQ may be better choices for the loss function. However, they are nondifferentiable functions which cannot be optimized by the conventional gradient descent algorithm. In this work, a gradient approximation method is used to calculate the gradients of the STOI and PESQ. Then the calculated gradients are used in the gradient descent algorithm to optimize the STOI and PESQ directly. Experimental results show the speech separation performance can be improved by the proposed method.