Author

A. Selitskiy

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Preprint Sep 2026

One-Step Voice Conversion by Learning kNN Transport in WavLM Space

Voice conversion (VC) systems fall into two families: non-parametric embedding-space methods, which need no trained model but degrade on short target utterances, and spectrogram-based neural architectures, which achieve strong quality via multi-module pipelines with tens of millions of parameters. We propose kNN-FM-VC,...

A. Selitskiy, David Millard · 0 citations

#artificial intelligence Sep 2025

Discrete optimal transport is a strong audio adversarial attack

Analysis using speaker similarity, Fr\'echet Audio Distance, and visualization of embedding distributions suggests that DOT succeeds by shifting source speech toward bona fide regions of the representation space rather than by maximizing speaker similarity.

A. Selitskiy, Akib Shahriyar, Jishnuraj Prakasan · arXiv.org · 0 citations

#machine learning May 2026

Stability of the Monge Map in Semi-Dual Optimal Transport

This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numerical solution is equivalent to solving a constrained optimization problem. We derive necessary and sufficient conditions for the convergence of Monge maps without requiring optimal...

A. Selitskiy, David Millard · arXiv.org · 0 citations