# CoSTA: Cognitive-State-Conditioned TTS Data Augmentation Using ASR Transcripts for Alzheimer's Disease Detection

arXiv: https://arxiv.org/abs/2606.06170 · HTML: https://mlsanity.com/paper/2606.06170/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models.
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Placed in

- Speech and audio language models / [ASR evaluation, bias and atypical speech](https://mlsanity.com/speech/asr-evaluation-bias-and-atypical-speech/)

## Builds on (most cited first, 19 of 19)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [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)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis](https://mlsanity.com/paper/2306.00814/) (2023)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Clever Hans Effect Found in Automatic Detection of Alzheimer's Disease through Speech](https://mlsanity.com/paper/2406.07410/) (2024)
- [Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?](https://mlsanity.com/paper/2401.05551/) (2024)
- [Data Augmentation for Dementia Detection in Spoken Language](https://mlsanity.com/paper/2206.12879/) (2022)
- [Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling](https://mlsanity.com/paper/2409.16937/) (2024)
- [Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives](https://mlsanity.com/paper/2501.03727/) (2025)
- [Leveraging Multimodal Methods and Spontaneous Speech for Alzheimer's Disease Identification](https://mlsanity.com/paper/2412.09928/) (2024)

## Abstract (arXiv)

Speech-based Alzheimer's Disease (AD) detection is constrained by scarce pathological speech data. To address this, we propose CoSTA, a Text-to-Speech (TTS)-based data augmentation framework. Specifically, we first develop two Cognitive-State-Conditioned (CS-Cond) TTS models by adapting CosyVoice2 and F5-TTS to synthesize speech with distinct AD and Healthy Control characteristics. Furthermore, by constructing a transcript pool comprising Manual Transcripts (MT) and 36 Automatic Speech Recognition (ASR) transcripts, we investigate the impact of text sources on TTS-based augmentation. We also perform augmentation-factor analysis and test-time augmentation. Experiments on the ADReSS dataset show that CS-Cond TTS significantly improves synthetic speech utility, and ASR-driven augmentation frequently outperforms MT-driven augmentation. Finally, CoSTA yields a 4.16% gain over the baseline, achieving an audio-only accuracy of 85.83% on the ADReSS test set and outperforming prior methods.