# Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis

arXiv: https://arxiv.org/abs/2601.14417 · HTML: https://mlsanity.com/paper/2601.14417/

## 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 (most cited first, 11 of 11)

- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer](https://mlsanity.com/paper/2409.00750/) (2024)
- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://mlsanity.com/paper/2305.18802/) (2023)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Accented Text-to-Speech Synthesis with Limited Data](https://mlsanity.com/paper/2305.04816/) (2023)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion](https://mlsanity.com/paper/2409.09352/) (2024)
- [DiEmo-TTS: Disentangled Emotion Representations via Self-Supervised Distillation for Cross-Speaker Emotion Transfer in Text-to-Speech](https://mlsanity.com/paper/2505.19687/) (2025)
- [Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters](https://mlsanity.com/paper/2401.05111/) (2024)
- [Pairwise Evaluation of Accent Similarity in Speech Synthesis](https://mlsanity.com/paper/2505.14410/) (2025)
- [Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis](https://mlsanity.com/paper/2406.10844/) (2024)

## Built on by (most cited first, 4 of 4)

- [Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data](https://mlsanity.com/paper/2603.07534/) (2026)
- [Learning-free L2-Accented Speech Generation using Phonological Rules](https://mlsanity.com/paper/2603.07550/) (2026)
- [Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost](https://mlsanity.com/paper/2604.25441/) (2026)
- [PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech](https://mlsanity.com/paper/2604.25476/) (2026)

## 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.