# Speaker recognition improvement using blind inversion of distortions

arXiv: https://arxiv.org/abs/2203.01164 · HTML: https://mlsanity.com/paper/2203.01164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Abstract (arXiv)

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results shows that a combination of data fusion with and without nonlinear distortion compensation can improve the recognition rates with saturated test sentences from 80% to 88.57%, while the results with clean speech (without saturation) is 87.76% for one microphone.