A Dual-path Conformer-Based Network for Neural Speech Coding

In this paper, we propose a neural speech coding method based on the dual-path conformer, which mainly consists of three steps: (1) the encoding and decoding of the time-frequency spectrum are performed by a structure that combines the CNN and the dual-path conformer, (2) residual vector quantization is employed to quantize the output features of encoder and form a compact discrete representation, and (3) multi-period and multi-scale discriminators are used to improve the perceptual quality of speech during adversarial training. Experimental results, from both subjective and objective evaluations, demonstrate that the proposed codec outperforms the state-of-the-art neural codec AudioDEC and the leading conventional codec Opus in terms of performance.