# The sound of my voice: speaker representation loss for target voice separation

arXiv: https://arxiv.org/abs/1911.02411 · HTML: https://mlsanity.com/paper/1911.02411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

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

- [Target Speech Extraction with Conditional Diffusion Model](https://mlsanity.com/paper/2308.03987/) (2023)
- [Learning source-aware representations of music in a discrete latent space](https://mlsanity.com/paper/2111.13321/) (2021)

## Abstract (arXiv)

Content and style representations have been widely studied in the field of style transfer. In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it speaker representation loss. The objective is to extract the target speaker voice from the noisy input and also remove it from the residual components. Compared to the conventional spectral reconstruction, our proposed framework maximizes the use of target speaker information by minimizing the distance between the speaker representations of reference and source separation output. We also propose triplet speaker representation loss as an additional criterion to remove the target speaker information from residual spectrogram output. VoiceFilter framework is adopted to evaluate source separation performance using the VCTK database, and we achieved improved performances compared to the baseline loss function without any additional network parameters.