# Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems

arXiv: https://arxiv.org/abs/1905.12531 · HTML: https://mlsanity.com/paper/1905.12531/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

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

- [Voice Conversion Can Improve ASR in Very Low-Resource Settings](https://mlsanity.com/paper/2111.02674/) (2021)
- [Improving Low-Resource Dialect Classification Using Retrieval-based Voice Conversion](https://mlsanity.com/paper/2507.03641/) (2025)

## Abstract (arXiv)

This paper evaluates the effectiveness of a Cycle-GAN based voice converter (VC) on four speaker identification (SID) systems and an automated speech recognition (ASR) system for various purposes. Audio samples converted by the VC model are classified by the SID systems as the intended target at up to 46% top-1 accuracy among more than 250 speakers. This encouraging result in imitating the target styles led us to investigate if converted (synthetic) samples can be used to improve ASR training. Unfortunately, adding synthetic data to the ASR training set only marginally improves word and character error rates. Our results indicate that even though VC models can successfully mimic the style of target speakers as measured by SID systems, improving ASR training with synthetic data from VC systems needs further research to establish its efficacy.