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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A Spectral Energy Distance for Parallel Speech Synthesis
Authors: Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,596 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,054 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we achieve state-of-the-art generation quality among implicit generative models, as judged by the recently-proposed c FDSD metric. When combining our method with adversarial techniques, we also improve upon the recently-proposed GAN-TTS model in terms of Mean Opinion Score as judged by trained human evaluators. We evaluate our proposed approach to speech synthesis by training 4 different models on the data set described in the previous section: |
| Researcher Affiliation | Industry | Alexey A. Gritsenko Tim Salimans Rianne van den Berg Jasper Snoek Nal Kalchbrenner EMAIL Google Research |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | An open source implementation of our generalized energy distance is available at https://github.com/google-research/google-research/ tree/master/ged_tts. |
| Open Datasets | No | Our TTS models are trained on a single-speaker North American English dataset, consisting of speech data spoken by a professional voice actor. The data consists of approximately sixty thousand utterances with durations ranging approximately from 0.5 seconds to 1 minute, and their corresponding aligned linguistic features and pitch information. The paper does not provide a specific name, URL, DOI, or formal citation (with authors and year for this dataset) for public access. |
| Dataset Splits | Yes | We report these metrics on both the training data as well as a larger validation set. We verified that this improvement is not due to overfitting by re-training the models on a smaller dataset (38.6 hours) and re-computing these metrics on a larger validation set (5.8 hours). |
| Hardware Specification | Yes | All of our models are trained on Cloud TPUs v3 with hyperparameters as described in Table 5 of the Appendix. |
| Software Dependencies | No | The paper mentions 'Our model parameters are updated using Adam [17]' but does not provide specific version numbers for software libraries, frameworks (e.g., TensorFlow, PyTorch), or programming languages used for implementation. |
| Experiment Setup | Yes | All of our models are trained on Cloud TPUs v3 with hyperparameters as described in Table 5 of the Appendix. Our model parameters are updated using Adam [17]. |