Abstract: Addressing the mismatch between the existing speech enhancement loss function and the evaluation index, the performance of the speech enhancement algorithm is effectively improved by combining the EEG component evaluation speech index with the loss function. Firstly, it is verified that the latency of mismatched negative waves of EEG components can be used as an objective evaluation index of speech. A latency function of mismatched negative waves is proposed, and it is connected to the signal-to-noise ratio, so as to solve the problem that the currently commonly used evaluation index cannot be directly used as a loss function to optimize the speech enhancement algorithm. Secondly, the latency function is trained jointly with the learning objectives in the traditional neural network, and the latency function is continuously optimized through training. Finally, the latency function is applied to the loss function of the discriminator that generates the adversarial network. Combining Conformer can effectively capture long-term dependencies and extract local features in both time and frequency dimensions. The experimental results show that the speech enhancement algorithm can effectively improve the speech characteristics by using the objective measures of EEG component evaluation. The effectiveness of the proposed algorithm is verified from the aspects of speech enhancement quality, intelligibility and distortion.

Key words: speech enhancement;mismatch , negativity;speech quality assessment;generative adversarial network