# Neural Speech Coding for Real-time Communications using Constant Bitrate Scalar Quantization

arXiv: https://arxiv.org/abs/2405.08417 · HTML: https://mlsanity.com/paper/2405.08417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2024.

## Built on by (most cited first, 4 of 4)

- [Scaling Transformers for Low-Bitrate High-Quality Speech Coding](https://mlsanity.com/paper/2411.19842/) (2024)
- [M-Prometheus: A Suite of Open Multilingual LLM Judges](https://mlsanity.com/paper/2504.04953/) (2025)
- [On the Design of Diffusion-based Neural Speech Codecs](https://mlsanity.com/paper/2504.08470/) (2025)
- [VP-VAE: Rethinking Vector Quantization via Adaptive Vector Perturbation](https://mlsanity.com/paper/2602.17133/) (2026)

## Abstract (arXiv)

Neural audio coding has emerged as a vivid research direction by promising good audio quality at very low bitrates unachievable by classical coding techniques. Here, end-to-end trainable autoencoder-like models represent the state of the art, where a discrete representation in the bottleneck of the autoencoder is learned. This allows for efficient transmission of the input audio signal. The learned discrete representation of neural codecs is typically generated by applying a quantizer to the output of the neural encoder. In almost all state-of-the-art neural audio coding approaches, this quantizer is realized as a Vector Quantizer (VQ) and a lot of effort has been spent to alleviate drawbacks of this quantization technique when used together with a neural audio coder. In this paper, we propose and analyze simple alternatives to VQ, which are based on projected Scalar Quantization (SQ). These quantization techniques do not need any additional losses, scheduling parameters or codebook storage thereby simplifying the training of neural audio codecs. For real-time speech communication applications, these neural codecs are required to operate at low complexity, low latency and at low bitrates. We address those challenges by proposing a new causal network architecture that is based on SQ and a Short-Time Fourier Transform (STFT) representation. The proposed method performs particularly well in the very low complexity and low bitrate regime.