Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fitting New Speakers Based on a Short Untranscribed Sample
Authors: Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results demonstrate a greatly improved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples. ... 5. Experiments |
| Researcher Affiliation | Collaboration | 1Facebook AI Research 2Tel Aviv University. Correspondence to: Eliya Nachmani <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Various samples can be found on the project s webpage https://ytaigman.github.io/fitspk/ index.html. |
| Open Datasets | Yes | The VCTK dataset (Veaux et al., 2017) contains 109 speakers. ... The Libri Speech dataset (Panayotov et al., 2015) is a corpus of 360 hours of voice... The Vox Celeb dataset (Nagrani et al., 2017) is a compilation of You Tube urls and time stamps... |
| Dataset Splits | No | The remaining eight speakers, which were left out for validation, are not used in our experiments. This indicates that a validation split was not used or provided for their specific experimental setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. It mentions using 'the crowd MOS toolkit by (P. Ribeiro et al., 2011)' but no version details. |
| Experiment Setup | Yes | The network has five convolutional layers of 3 3 filters, each with 32 channels. ... in all of our experiments, we set α = β = 10. ... during the first phase, a noise SD equal to 4.0 is added ... these sequence are cropped to a length of 100. A batch size equal to 256 is used for exactly 90 epochs. Phase 2 of the training process employs noise SD of 2.0, and sequence lengths that are trimmed at 1000 vocoder features. The batch size is reduced to 30... |