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Recently, speech codecs based on neural networks have proven to performbetter than traditional methods. However, redundancy in traditional parameterquantization is visible within the codec architecture of combining thetraditional codec with the neural vocoder. In this paper, we propose a novelframework named CQNV, which combines the coarsely quantized parameters of atraditional parametric codec to reduce the bitrate with a neural vocoder toimprove the quality of the decoded speech. Furthermore, we introduce aparameters processing module into the neural vocoder to enhance the applicationof the bitstream of traditional speech coding parameters to the neural vocoder,further improving the reconstructed speech's quality. In the experiments, bothsubjective and objective evaluations demonstrate the effectiveness of theproposed CQNV framework. Specifically, our proposed method can achieve higherquality reconstructed speech at 1.1 kbps than Lyra and Encodec at 3 kbps.

CQNV: A combination of coarsely quantized bitstream and neural vocoder for low rate speech coding