# Voice Conversion Can Improve ASR in Very Low-Resource Settings arXiv: https://arxiv.org/abs/2111.02674 ยท HTML: https://mlsanity.com/paper/2111.02674/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 39 references that are in the map. - First posted on arXiv 4 Nov 2021. ## Builds on (most cited first, 24 of 24) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021) - [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019) - [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019) - [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020) - [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017) - [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020) - [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019) - [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021) - [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017) - [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021) - [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017) - [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019) - [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020) - [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020) - [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020) - [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020) - [You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation](https://mlsanity.com/paper/2005.07157/) (2020) - [Low-resource expressive text-to-speech using data augmentation](https://mlsanity.com/paper/2011.05707/) (2020) - [Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing](https://mlsanity.com/paper/2108.00917/) (2021) - [Towards Natural Bilingual and Code-Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross-Lingual Voice Conversion](https://mlsanity.com/paper/2010.08136/) (2020) - [StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts](https://mlsanity.com/paper/2106.00043/) (2021) - [Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems](https://mlsanity.com/paper/1905.12531/) (2019) ## Built on by (most cited first, 14 of 14) - [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023) - [ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion](https://mlsanity.com/paper/2204.00618/) (2022) - [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024) - [An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR](https://mlsanity.com/paper/2503.08954/) (2025) - [Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives](https://mlsanity.com/paper/2501.06478/) (2025) - [Non-Parallel Voice Conversion for ASR Augmentation](https://mlsanity.com/paper/2209.06987/) (2022) - [Iteratively Improving Speech Recognition and Voice Conversion](https://mlsanity.com/paper/2305.15055/) (2023) - [Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR](https://mlsanity.com/paper/2506.04364/) (2025) - [From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition](https://mlsanity.com/paper/2505.16972/) (2025) - [Improving Low-Resource Dialect Classification Using Retrieval-based Voice Conversion](https://mlsanity.com/paper/2507.03641/) (2025) - [Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains](https://mlsanity.com/paper/2606.11429/) (2026) - [Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification](https://mlsanity.com/paper/2505.24713/) (2025) - [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2606.21340/) (2026) - [Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR](https://mlsanity.com/paper/2610.13807/) (2026) ## Abstract (arXiv) Voice conversion (VC) could be used to improve speech recognition systems in low-resource languages by using it to augment limited training data. However, VC has not been widely used for this purpose because of practical issues such as compute speed and limitations when converting to and from unseen speakers. Moreover, it is still unclear whether a VC model trained on one well-resourced language can be applied to speech from another low-resource language for the aim of data augmentation. In this work we assess whether a VC system can be used cross-lingually to improve low-resource speech recognition. We combine several recent techniques to design and train a practical VC system in English, and then use this system to augment data for training speech recognition models in several low-resource languages. When using a sensible amount of VC augmented data, speech recognition performance is improved in all four low-resource languages considered. We also show that VC-based augmentation is superior to SpecAugment (a widely used signal processing augmentation method) in the low-resource languages considered.