Recently, speech codecs based on neural networks have proven to perform better than traditional methods. However, redundancy in traditional parameter quantization is visible within the codec architecture of combining the traditional codec with the neural vocoder. In this paper, we propose a novel framework named CQNV, which combines the coarsely quantized parameters of a traditional parametric codec to reduce the bitrate with a neural vocoder to improve the quality of the decoded speech. Furthermore, we introduce a parameters processing module into the neural vocoder to enhance the application of the bitstream of traditional speech coding parameters to the neural vocoder, further improving the reconstructed speech's quality. In the experiments, both subjective and objective evaluations demonstrate the effectiveness of the proposed CQNV framework. Specifically, our proposed method can achieve higher quality reconstructed speech at 1.1 kbps than Lyra and Encodec at 3 kbps.

翻译：近年来，基于神经网络的语音编解码器已被证明比传统方法性能更优。然而，在结合传统编解码器与神经声码器的编解码架构中，传统参数量化存在的冗余问题仍较为显著。本文提出一种名为CQNV的新型框架，该框架将传统参数编解码器的粗量化参数（用于降低比特率）与神经声码器（用于提升解码语音质量）相结合。此外，我们在神经声码器中引入参数处理模块，以增强传统语音编码参数比特流在神经声码器中的应用效果，从而进一步提升重建语音质量。实验结果表明，所提出的CQNV框架在主观与客观评估中均展现出有效性。具体而言，本方法在1.1 kbps比特率下能够获得比Lyra和Encodec在3 kbps比特率下更高质量的重建语音。