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Robustness of Voice Conversion Techniques Under Mismatched Conditions

Sound 2016-12-23 v1 Machine Learning Machine Learning

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.

Keywords

Cite

@article{arxiv.1612.07523,
  title  = {Robustness of Voice Conversion Techniques Under Mismatched Conditions},
  author = {Monisankha Pal and Dipjyoti Paul and Md Sahidullah and Goutam Saha},
  journal= {arXiv preprint arXiv:1612.07523},
  year   = {2016}
}

Comments

5 pages, 2 figures

R2 v1 2026-06-22T17:32:08.111Z