mlsanityData to 14 Oct 2026

Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis

arXiv 2601.14417 ↗First posted 20 Jan 2026cs.CLcited by 4

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Abstract (arXiv)

Many spoken languages, including English, exhibit wide variation in dialects and accents, making accent control an important capability for flexible text-to-speech (TTS) models. Current TTS systems typically generate accented speech by conditioning on speaker embeddings associated with specific accents. While effective, this approach offers limited interpretability and controllability, as embeddings also encode traits such as timbre and emotion. In this study, we analyze the interaction between speaker embeddings and linguistically motivated phonological rules in accented speech synthesis. Using American and British English as a case study, we implement rules for flapping, rhoticity, and vowel correspondences. We propose the phoneme shift rate (PSR), a novel metric quantifying how strongly embeddings preserve or override rule-based transformations. Experiments show that combining rules with embeddings yields more authentic accents, while embeddings can attenuate or overwrite rules, revealing entanglement between accent and speaker identity. Our findings highlight rules as a lever for accent control and a framework for evaluating disentanglement in speech generation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 20 Jan 2026.

Builds on

11 of 24 references.

References · 11

  1. 2022UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022cited by 390
  2. 2024MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformercited by 161
  3. 2023LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpuscited by 101
  4. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  5. 2023Accented Text-to-Speech Synthesis with Limited Datacited by 12
  6. 2024AccentBox: Towards High-Fidelity Zero-Shot Accent Generationcited by 9
  7. 2024MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversioncited by 6
  8. 2025DiEmo-TTS: Disentangled Emotion Representations via Self-Supervised Distillation for Cross-Speaker Emotion Transfer in Text-to-Speechcited by 5
  9. 2024Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapterscited by 4
  10. 2025Pairwise Evaluation of Accent Similarity in Speech Synthesiscited by 4
  11. 2024Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesiscited by 3

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Citing papers per year: 2026: 4.

4 papers.