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..
SSDM: Scalable Speech Dysfluency Modeling
Authors: Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Paul Baquirin, Zachary Miller, Maria Luisa Gorno Tempini, Gopala Anumanchipalli
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate phonetic transcription (forced alignment) performance using simulated data from VCTK++[1] and our proposed Libri-Dys dataset. The framewise F1 score and d PER[1] are used as evaluation metrics. Five types of training data are used: VCTK++, Libri TTS (100%, [106]), Libri-Dys (30%), Libri-Dys (60%), and Libri-Dys (100%). |
| Researcher Affiliation | Academia | Jiachen Lian1, Xuanru Zhou2, Zoe Ezzes3, Jet Vonk3, Brittany Morin3, David Baquirin3, Zachary Miller3, Maria Luisa Gorno Tempini3, Gopala Anumanchipalli1 1 UC Berkeley, 2 Zhejiang University, 3 UCSF |
| Pseudocode | Yes | Algorithm 1 Find Longest Common Subsequence (LCS) |
| Open Source Code | No | For code, we are waiting for the other approval. |
| Open Datasets | Yes | Data is opensourced at https://bit.ly/4ao Ld WU. |
| Dataset Splits | No | For training, we use VCTK++[1] and Libri-Dys datasets. For testing, we randomly sample 10% of the training data. The paper does not explicitly describe a separate validation split or how it's derived. |
| Hardware Specification | Yes | The training is conducted using two A6000 GPUs. |
| Software Dependencies | No | The paper mentions software like Wav LM, Glow algorithm, and Adam optimizer but does not provide specific version numbers for general software dependencies (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | In Eq. 2, τ = 2. In Eq. 4, a = b = 1, mrow = 3. In Eq. 6 and Eq. 7, we simply set K1 = K2 = 1. In Eq. 8, λ1 = λ2 = λ3 = 1. In Eq. 12 and Eq. 13, δ = 0.9. ... We use the Adam optimizer and decay the learning rate from 0.001 at a rate of 0.9 every 10 steps until convergence. |