Summary of Preference Alignment Improves Language Model-based Tts, by Jinchuan Tian et al.
Preference Alignment Improves Language Model-Based TTS
by Jinchuan Tian, Chunlei Zhang, Jiatong Shi, Hao Zhang, Jianwei Yu, Shinji Watanabe, Dong Yu
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This study investigates how preference alignment algorithms can optimize language model (LM)-based text-to-speech (TTS) systems, particularly Direct Preference Optimization (DPO). The researchers use a 1.15 billion parameter LM-based TTS model and show that preference alignment consistently improves intelligibility, speaker similarity, and subjective evaluation scores. These metrics even surpass human speech in some evaluations. Additionally, the study demonstrates the applicability of preference alignment to low-resource scenarios and its effectiveness in generalizing to out-of-domain applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper looks at how to make text-to-speech technology better by aligning language models with what people want to hear. The team used a super smart language model that can generate speech, and they showed that this alignment makes the generated speech sound more natural, like human speech. They also found that this method works well even when there’s not much data available and can be applied to different situations. |
Keywords
» Artificial intelligence » Alignment » Language model » Optimization
