# Imitation Learning for Elder-Facing Speech Synthesis

arXiv: https://arxiv.org/abs/2606.21053 · HTML: https://mlsanity.com/paper/2606.21053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Seed-TTS: A Family of High-Quality Versatile Speech Generation Models](https://mlsanity.com/paper/2406.02430/) (2024)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (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)
- [CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training](https://mlsanity.com/paper/2505.17589/) (2025)
- [FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs](https://mlsanity.com/paper/2407.04051/) (2024)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [SpeechAlign: Aligning Speech Generation to Human Preferences](https://mlsanity.com/paper/2404.05600/) (2024)
- [Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis](https://mlsanity.com/paper/1910.10288/) (2019)
- [Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization](https://mlsanity.com/paper/2409.10157/) (2024)
- [Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2503.22230/) (2025)
- [F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization](https://mlsanity.com/paper/2504.02407/) (2025)
- [Preference Alignment Improves Language Model-Based TTS](https://mlsanity.com/paper/2409.12403/) (2024)
- [Advancing Zero-shot Text-to-Speech Intelligibility across Diverse Domains via Preference Alignment](https://mlsanity.com/paper/2505.04113/) (2025)
- [Imitation Learning by Reinforcement Learning](https://mlsanity.com/paper/2108.04763/) (2021)
- [Fine-Tuning Language Models with Reward Learning on Policy](https://mlsanity.com/paper/2403.19279/) (2024)
- [WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation](https://mlsanity.com/paper/2509.03959/) (2025)
- [TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems](https://mlsanity.com/paper/2506.19441/) (2025)
- [Group Relative Policy Optimization for Text-to-Speech with Large Language Models](https://mlsanity.com/paper/2509.18798/) (2025)
- [DrawSpeech: Expressive Speech Synthesis Using Prosodic Sketches as Control Conditions](https://mlsanity.com/paper/2501.04256/) (2025)
- [Fine-Tuning Text-to-Speech Diffusion Models Using Reinforcement Learning with Human Feedback](https://mlsanity.com/paper/2508.03123/) (2025)

## Abstract (arXiv)

Recent advances in text-to-speech (TTS) synthesis have achieved highly natural and expressive speech generation. However, these systems are designed for general adults and overlook older adults' speech comprehension needs due to age-related sensory and cognitive decline. Prior work involves older adults by collecting preference feedback to tune model parameters. However, obtaining sufficient preference data is costly and difficult, as older adults quickly become fatigued during collection. In this paper, we propose a novel imitation learning (IL) framework to learn TTS models from expert demonstrations. We further improve Group Relative Policy Optimization (GRPO) with two-stage on-policy reward learning (OPRL) to mitigate reward hacking under limited supervision from expert demonstration. Experimental results show that GRPO w/ OPRL outperforms GRPO and supervised baselines in objective and subjective metrics. Audio samples are available at https://dongru1.github.io/demo/im-efss