# Modeling strategies for speech enhancement in the latent space of a neural audio codec
This repository provides the official model checkpoints for the paper *[Modeling strategies for speech enhancement in the latent space of a neural audio codec](https://arxiv.org/abs/2510.26299)* authored by Sofiene Kammoun, Xavier Alameda-Pineda, and Simon Leglaive, and published at IEEE ICASSP 2026.
We explore different modeling strategies (autoregressive vs. non-autoregressive) and representation spaces (discrete vs. continuous) for speech enhancement using neural audio codecs and Conformer-based architectures.
[arXiv](https://arxiv.org/abs/2510.26299) | [Code and Audio examples](https://sofienekammoun.github.io/SE-NAC-25/) | [Bibtex](#citation)
## Overview
Our work introduces and compares a family of speech enhancement models that systematically vary along two main axes:
- **Representation Type**
- Discrete tokens
- Continuous latent vectors
- **Modeling Strategy**
- Autor…
# Modeling strategies for speech enhancement in the latent space of a neural audio codec
This repository provides the official model checkpoints of the paper *[Modeling strategies for speech enhancement in the latent space of a neural audio codec](https://arxiv.org/abs/2510.26299)* authored by Sofiene Kammoun, Xavier Alameda-Pineda, and Simon Leglaive, and published at IEEE ICASSP 2026.
We explore different modeling strategies (autoregressive vs. non-autoregressive) and representation spaces (discrete vs. continuous) for speech enhancement using neural audio codecs and Conformer-based architectures.
[arXiv](https://arxiv.org/abs/2510.26299) | [Audio examples](https://sofienekammoun.github.io/SE-NAC-25/) | [Bibtex](#citation)
## Overview
Our work introduces and compares a family of speech enhancement models that systematically vary along two main axes:
- **Representation Type**
- Discrete tokens
- Continuous latent vectors
- **Modeling Strategy**
- Autoregressive …
