# Wavenet based low rate speech coding

arXiv: https://arxiv.org/abs/1712.01120 · HTML: https://mlsanity.com/paper/1712.01120/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 30 of 33)

- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [It's Raw! Audio Generation with State-Space Models](https://mlsanity.com/paper/2202.09729/) (2022)
- [COIN++: Neural Compression Across Modalities](https://mlsanity.com/paper/2201.12904/) (2022)
- [WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration](https://mlsanity.com/paper/2210.01029/) (2022)
- [FlowDec: A flow-based full-band general audio codec with high perceptual quality](https://mlsanity.com/paper/2503.01485/) (2025)
- [Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem](https://mlsanity.com/paper/2112.09382/) (2021)
- [Disentangled Feature Learning for Real-Time Neural Speech Coding](https://mlsanity.com/paper/2211.11960/) (2022)
- [Generative De-Quantization for Neural Speech Codec via Latent Diffusion](https://mlsanity.com/paper/2311.08330/) (2023)
- [Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders](https://mlsanity.com/paper/2102.06610/) (2021)
- [WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)](https://mlsanity.com/paper/2106.08887/) (2021)
- [HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding](https://mlsanity.com/paper/2107.10843/) (2021)
- [End-to-End Neural Speech Coding for Real-Time Communications](https://mlsanity.com/paper/2201.09429/) (2022)
- [Latent-Domain Predictive Neural Speech Coding](https://mlsanity.com/paper/2207.08363/) (2022)
- [Scalable and Efficient Neural Speech Coding: A Hybrid Design](https://mlsanity.com/paper/2103.14776/) (2021)
- [A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate](https://mlsanity.com/paper/2108.04051/) (2021)
- [Ultra-Low-Bitrate Speech Coding with Pretrained Transformers](https://mlsanity.com/paper/2207.02262/) (2022)
- [Gull: A Generative Multifunctional Audio Codec](https://mlsanity.com/paper/2404.04947/) (2024)
- [End-to-End Binaural Speech Synthesis](https://mlsanity.com/paper/2207.03697/) (2022)
- [Cross-Scale Vector Quantization for Scalable Neural Speech Coding](https://mlsanity.com/paper/2207.03067/) (2022)
- [NESC: Robust Neural End-2-End Speech Coding with GANs](https://mlsanity.com/paper/2207.03282/) (2022)
- [FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates](https://mlsanity.com/paper/2409.17635/) (2024)
- [Neural Speech and Audio Coding: Modern AI Technology Meets Traditional Codecs](https://mlsanity.com/paper/2408.06954/) (2024)
- [Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset](https://mlsanity.com/paper/2209.06358/) (2022)
- [Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound](https://mlsanity.com/paper/2211.08715/) (2022)
- [MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning](https://mlsanity.com/paper/2306.12785/) (2023)
- [AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes](https://mlsanity.com/paper/2308.07221/) (2023)
- [Audio Decoding by Inverse Problem Solving](https://mlsanity.com/paper/2409.07858/) (2024)

## Abstract (arXiv)

Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operating at 2.4 kb/s. We compare this parametric coder with a waveform coder based on the same generative model and show that approximating the signal waveform incurs a large rate penalty. Our experiments confirm the high performance of the WaveNet based coder and show that the speech produced by the system is able to additionally perform implicit bandwidth extension and does not significantly impair recognition of the original speaker for the human listener, even when that speaker has not been used during the training of the generative model.