  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.
