# An Evaluation Framework for Text-to-Speech Voice Reconstruction

arXiv: https://arxiv.org/abs/2606.21343 · HTML: https://mlsanity.com/paper/2606.21343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 52 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 29 of 29)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [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)
- [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://mlsanity.com/paper/2306.07691/) (2023)
- [E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS](https://mlsanity.com/paper/2406.18009/) (2024)
- [VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild](https://mlsanity.com/paper/2403.16973/) (2024)
- [Qwen3-TTS Technical Report](https://mlsanity.com/paper/2601.15621/) (2026)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2506.21619/) (2025)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Better speech synthesis through scaling](https://mlsanity.com/paper/2305.07243/) (2023)
- [Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis](https://mlsanity.com/paper/2411.01156/) (2024)
- [Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement](https://mlsanity.com/paper/2502.07243/) (2025)
- [OpenVoice: Versatile Instant Voice Cloning](https://mlsanity.com/paper/2312.01479/) (2023)
- [TTSDS -- Text-to-Speech Distribution Score](https://mlsanity.com/paper/2407.12707/) (2024)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.12991/) (2025)
- [DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model](https://mlsanity.com/paper/2506.00350/) (2025)
- [CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction](https://mlsanity.com/paper/2406.08336/) (2024)
- [Good practices for evaluation of synthesized speech](https://mlsanity.com/paper/2503.03250/) (2025)
- [The State Of TTS: A Case Study with Human Fooling Rates](https://mlsanity.com/paper/2508.04179/) (2025)
- [Can we reconstruct a dysarthric voice with the large speech model Parler TTS?](https://mlsanity.com/paper/2506.04397/) (2025)
- [Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation](https://mlsanity.com/paper/2509.19231/) (2025)
- [Creating Personalized Synthetic Voices from Articulation Impaired Speech Using Augmented Reconstruction Loss](https://mlsanity.com/paper/2401.03816/) (2024)
- [Facilitating Personalized TTS for Dysarthric Speakers Using Knowledge Anchoring and Curriculum Learning](https://mlsanity.com/paper/2508.10412/) (2025)
- [Layer-wise Analysis for Quality of Multilingual Synthesized Speech](https://mlsanity.com/paper/2509.04830/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Cross Community Agenda for Speech AI](https://mlsanity.com/paper/2609.13168/) (2026)

## Abstract (arXiv)

Voice reconstruction using Text-to-Speech (TTS) offers a communication method for people with speech disorders, which aims to retain their speaker identity while improving intelligibility. Previous work generally relies on Mean Opinion Score (MOS) to evaluate naturalness and speaker similarity, but this has limited sensitivity and reliability. We propose an evaluation framework with subjective and objective components. Subjectively, we evaluate perceived intelligibility and speaker identity using Best Worst Scaling (BWS) with situational framing. Objectively, we demonstrate that standard measures fail to predict reconstruction success for highly unintelligible speakers, so we introduce a novel dual-reference distributional measure to assess the trade-off between intelligibility and speaker identity. By evaluating the output of 17 zero-shot TTS systems for 193 speakers, we show that our framework provides a reliable and task-aligned approach for assessing voice reconstruction.