mlsanityData to 7 Oct 2026

Acoustic and perceptual differences between standard and accented speech and their voice clones

arXiv 2604.01562 ↗First posted 2 Apr 2026cs.SD cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences. We compare standard and heavily accented Mandarin speech and their voice clones using a combined computational and perceptual design. Embedding-based analyses showed larger original-clone distances for accented speakers in several speaker-discriminative embedding spaces, but this difference disappeared after adjusting for each speaker's within-original baseline variability. In the perception study, clones are rated as more similar to their originals for standard than for accented speakers, and intelligibility increases from original to clone, with a larger gain for accented speech. These results show that accent variation can shape perceived identity match and intelligibility in voice cloning even when it is not observed in baseline-adjusted speaker-embedding distance, and they motivate treating accent preservation as an explicit component of speaker identity preservation, rather than assuming that it is fully captured by off-the-shelf speaker-discriminative embeddings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

20 of 59 references.

References · 20

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,609
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
  4. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  5. 2020An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learningcited by 111
  6. 2020AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselinescited by 111
  7. 2020Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversioncited by 75
  8. 2018The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methodscited by 68
  9. 2021UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Trainingcited by 43
  10. 2022Do self-supervised speech models develop human-like perception biases?cited by 28
  11. 2023Warning: Humans Cannot Reliably Detect Speech Deepfakescited by 20
  12. 2025"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Servicescited by 9
8 more
  1. 2024Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Modelcited by 8
  2. 2025Forensic deepfake audio detection using segmental speech featurescited by 8
  3. 2026Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detectioncited by 5
  4. 2023Evaluating Methods for Ground-Truth-Free Foreign Accent Conversioncited by 4
  5. 2025Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matchingcited by 4
  6. 2026Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbationcited by 3
  7. 2022Automatic Evaluation of Speaker Similaritycited by 2
  8. 2024SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Modelscited by 1

Built on by

Not cited yet.