Zero-Shot Face-Based Voice Conversion: Bottleneck-Free Speech Disentanglement in the Real-World Scenario
Authors: Shao-En Weng, Hong-Han Shuai, Wen-Huang Cheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative experiments show that our method outperforms previous work. |
| Researcher Affiliation | Academia | Shao-En Weng, Hong-Han Shuai, Wen-Huang Cheng National Yang Ming Chiao Tung University anita4213.ee09@nycu.edu.tw, hhshuai@nycu.edu.tw, whcheng@nycu.edu.tw |
| Pseudocode | No | The paper describes the model architecture and training strategy in text and diagrams, but it does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper provides a link to a demo website for audio samples (https://sites.google.com/view/spfacevc-demo/) but does not explicitly state that the source code for the methodology is openly available or provide a link to a code repository. |
| Open Datasets | Yes | LRS3 (Afouras, Chung, and Zisserman 2018) dataset is collected from TED and TEDx videos downloaded from You Tube. |
| Dataset Splits | No | The paper mentions using 'training speakers' (100, 200, 400) and 'unseen utterances' for evaluation, but it does not specify explicit training, validation, and test dataset splits as percentages or sample counts to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions specific software components like Waveglow, ADAM, and Parselmouth library, but it does not provide their specific version numbers required for reproducible software dependencies. |
| Experiment Setup | Yes | The learning rate for the generator is set to 0.0001. For the discriminator, it is set to 0.0004 and with β1 = 0.9, β2 = 0.999. [...] Empirically, we set α = 1, β = 0.1, γ = 100, and δ = 0.1. [...] Here, we set the batch size to 2. |