Summary of Ella-v: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering, by Yakun Song et al.
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
by Yakun Song, Zhuo Chen, Xiaofei Wang, Ziyang Ma, Xie Chen
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 The proposed ELLA-V framework is a zero-shot text-to-speech (TTS) model that leverages language models like VALL-E to generate synthesized speech. Unlike existing methods, ELLA-V allows for fine-grained control over the output by interleaving phoneme and acoustic tokens. This approach addresses limitations such as repetitions, omissions, and silence generation in previous models. Experimental results show that ELLA-V outperforms VALL-E in terms of accuracy and stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new text-to-speech model called ELLA-V. It’s like a computer program that can turn words into speech. The problem with current methods is that they can get stuck repeating the same sound or leave long silences. To fix this, ELLA-V mixes up the order of sounds and words to make it more natural. This new approach works better than other models and lets you control the speech more precisely. |
Keywords
* Artificial intelligence * Zero shot
