# Pairwise Evaluation of Accent Similarity in Speech Synthesis

arXiv: https://arxiv.org/abs/2505.14410 · HTML: https://mlsanity.com/paper/2505.14410/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 24 of 24)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer](https://mlsanity.com/paper/2409.00750/) (2024)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [Deep Learning Based Assessment of Synthetic Speech Naturalness](https://mlsanity.com/paper/2104.11673/) (2021)
- [Amphion: An Open-Source Audio, Music and Speech Generation Toolkit](https://mlsanity.com/paper/2312.09911/) (2023)
- [CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice](https://mlsanity.com/paper/2305.18283/) (2023)
- [Voice-preserving Zero-shot Multiple Accent Conversion](https://mlsanity.com/paper/2211.13282/) (2022)
- [Accented Text-to-Speech Synthesis with Limited Data](https://mlsanity.com/paper/2305.04816/) (2023)
- [Convert and Speak: Zero-shot Accent Conversion with Minimum Supervision](https://mlsanity.com/paper/2408.10096/) (2024)
- [TTS-Guided Training for Accent Conversion Without Parallel Data](https://mlsanity.com/paper/2212.10204/) (2022)
- [High-Fidelity Neural Phonetic Posteriorgrams](https://mlsanity.com/paper/2402.17735/) (2024)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network](https://mlsanity.com/paper/2212.05751/) (2022)
- [MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion](https://mlsanity.com/paper/2409.09352/) (2024)
- [Transfer the linguistic representations from TTS to accent conversion with non-parallel data](https://mlsanity.com/paper/2401.03538/) (2024)
- [AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents](https://mlsanity.com/paper/2210.17305/) (2022)
- [Parameter-Efficient Learning for Text-to-Speech Accent Adaptation](https://mlsanity.com/paper/2305.11320/) (2023)
- [DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech](https://mlsanity.com/paper/2410.13342/) (2024)
- [Location, Location: Enhancing the Evaluation of Text-to-Speech Synthesis Using the Rapid Prosody Transcription Paradigm](https://mlsanity.com/paper/2107.02527/) (2021)

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

- [Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis](https://mlsanity.com/paper/2601.14417/) (2026)
- [Rethinking Discrete Speech Representation Tokens for Accent Generation](https://mlsanity.com/paper/2601.19786/) (2026)
- [Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages](https://mlsanity.com/paper/2604.21481/) (2026)
- [PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech](https://mlsanity.com/paper/2604.25476/) (2026)

## Abstract (arXiv)

Despite growing interest in generating high-fidelity accents, evaluating accent similarity in speech synthesis has been underexplored. We aim to enhance both subjective and objective evaluation methods for accent similarity. Subjectively, we refine the XAB listening test by adding components that achieve higher statistical significance with fewer listeners and lower costs. Our method involves providing listeners with transcriptions, having them highlight perceived accent differences, and implementing meticulous screening for reliability. Objectively, we utilise pronunciation-related metrics, based on distances between vowel formants and phonetic posteriorgrams, to evaluate accent generation. Comparative experiments reveal that these metrics, alongside accent similarity, speaker similarity, and Mel Cepstral Distortion, can be used. Moreover, our findings underscore significant limitations of common metrics like Word Error Rate in assessing underrepresented accents.