Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing

Understand

The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications.

• Therefore, our study applies a modified Transformer in a speech enhancement task.
• Specifically, positional encoding in the Transformer may not be necessary for speech enhancement, and hence, it is replaced by convolutional layers.
• To further improve the perceptual evaluation of the speech quality (PESQ) scores of enhanced speech, the L_1 pre-trained Transformer is fine-tuned using a MetricGAN framework.