# Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation

arXiv: https://arxiv.org/abs/2509.19231 · HTML: https://mlsanity.com/paper/2509.19231/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [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)
- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation](https://mlsanity.com/paper/1904.04169/) (2019)
- [UltraSuite: A Repository of Ultrasound and Acoustic Data from Child Speech Therapy Sessions](https://mlsanity.com/paper/1907.00835/) (2019)
- [The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.04908/) (2022)
- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training](https://mlsanity.com/paper/2001.04260/) (2020)
- [SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines](https://mlsanity.com/paper/2111.03811/) (2021)
- [Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR](https://mlsanity.com/paper/2501.10256/) (2025)
- [Can we reconstruct a dysarthric voice with the large speech model Parler TTS?](https://mlsanity.com/paper/2506.04397/) (2025)
- [Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech](https://mlsanity.com/paper/1810.12656/) (2018)

## Built on by (most cited first, 3 of 3)

- [PRiSM: Benchmarking Phone Realization in Speech Models](https://mlsanity.com/paper/2601.14046/) (2026)
- [Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling](https://mlsanity.com/paper/2601.21205/) (2026)
- [An Evaluation Framework for Text-to-Speech Voice Reconstruction](https://mlsanity.com/paper/2606.21343/) (2026)

## Abstract (arXiv)

We present ChiReSSD, a speech reconstruction framework that preserves children speaker's identity while suppressing mispronunciations. Unlike prior approaches trained on healthy adult speech, ChiReSSD adapts to the voices of children with speech sound disorders (SSD), with particular emphasis on pitch and prosody. We evaluate our method on the STAR dataset and report substantial improvements in lexical accuracy and speaker identity preservation. Furthermore, we automatically predict the phonetic content in the original and reconstructed pairs, where the proportion of corrected consonants is comparable to the percentage of correct consonants (PCC), a clinical speech assessment metric. Our experiments show Pearson correlation of 0.63 between automatic and human expert annotations, highlighting the potential to reduce the manual transcription burden. In addition, experiments on the TORGO dataset demonstrate effective generalization for reconstructing adult dysarthric speech. Our results indicate that disentangled, style-based TTS reconstruction can provide identity-preserving speech across diverse clinical populations.