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

arXiv: https://arxiv.org/abs/2307.13295 · HTML: https://mlsanity.com/paper/2307.13295/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 25 Jul 2023.

## Built on by (most cited first, 2 of 2)

- [Fewer-token Neural Speech Codec with Time-invariant Codes](https://mlsanity.com/paper/2310.00014/) (2023)
- [SuperCodec: A Neural Speech Codec with Selective Back-Projection Network](https://mlsanity.com/paper/2407.20530/) (2024)

## Abstract (arXiv)

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.