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..
Beyond Speech: Generalizing D-Vectors for Biometric Verification
Authors: Jacob Baldwin, Ryan Burnham, Andrew Meyer, Robert Dora, Robert Wright842-849
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains. |
| Researcher Affiliation | Industry | Jacob Baldwin, Ryan Burnham, Andrew Meyer, Robert Dora, Robert Wright Assured Information Security, Inc. 153 Brooks Rd. Rome, NY 13441 EMAIL |
| Pseudocode | No | The paper describes the model architectures and framework using diagrams (Figure 1, 2, 3) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | Additionally, we use two publicly available datasets as benchmarks one for keystroke (Clarkson) (Murphy et al. 2017) and gait (UCI) (Anguita et al. 2013). |
| Dataset Splits | No | The paper states "To train the D-Vectors models, the subjects are randomly partitioned into 70% for training and 30% for testing." and "A training-test split of 70/30% of the subject data is performed on the Multimod dataset", but does not explicitly describe a separate validation dataset split. |
| Hardware Specification | No | The paper mentions "On a modern dual-CPU machine with GPU acceleration" but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper describes various models and methods but does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | Dropout is applied aggressively, 75%, to this last layer to prevent over-fitting. and Finally, dropout is applied to each DNN layer, 50% on the first layer and 75% on the remaining two layers, to prevent over-fitting. |