Robustness of Voice Conversion Techniques Under Mismatched Conditions

TL;DR

This paper evaluates the robustness of various voice conversion techniques under mismatched and noisy conditions, revealing performance degradation and potential improvements through speech enhancement methods.

Contribution

It provides a comprehensive comparison of VC methods under mismatched conditions and assesses speech enhancement techniques for robustness.

Findings

VC performance degrades in noisy environments

BLFWAS outperforms other VC methods in noise

Speech enhancement techniques can improve VC robustness

Abstract

Most of the existing studies on voice conversion (VC) are conducted in acoustically matched conditions between source and target signal. However, the robustness of VC methods in presence of mismatch remains unknown. In this paper, we report a comparative analysis of different VC techniques under mismatched conditions. The extensive experiments with five different VC techniques on CMU ARCTIC corpus suggest that performance of VC methods substantially degrades in noisy conditions. We have found that bilinear frequency warping with amplitude scaling (BLFWAS) outperforms other methods in most of the noisy conditions. We further explore the suitability of different speech enhancement techniques for robust conversion. The objective evaluation results indicate that spectral subtraction and log minimum mean square error (logMMSE) based speech enhancement techniques can be used to improve the performance in specific noisy conditions.