# Personalized Neural Speech Codec

arXiv: https://arxiv.org/abs/2404.00791 · HTML: https://mlsanity.com/paper/2404.00791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2024.

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

- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)

## Abstract (arXiv)

In this paper, we propose a personalized neural speech codec, envisioning that personalization can reduce the model complexity or improve perceptual speech quality. Despite the common usage of speech codecs where only a single talker is involved on each side of the communication, personalizing a codec for the specific user has rarely been explored in the literature. First, we assume speakers can be grouped into smaller subsets based on their perceptual similarity. Then, we also postulate that a group-specific codec can focus on the group's speech characteristics to improve its perceptual quality and computational efficiency. To this end, we first develop a Siamese network that learns the speaker embeddings from the LibriSpeech dataset, which are then grouped into underlying speaker clusters. Finally, we retrain the LPCNet-based speech codec baselines on each of the speaker clusters. Subjective listening tests show that the proposed personalization scheme introduces model compression while maintaining speech quality. In other words, with the same model complexity, personalized codecs produce better speech quality.