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..
Predicting Latent Narrative Mood Using Audio and Physiologic Data
Authors: Tuka AlHanai, Mohammad Ghassemi
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study we utilized a combination of auditory, text, and physiological signals to predict the mood (happy or sad) of 31 narrations from subjects engaged in personal story-telling. We extracted 386 audio and 222 physiological features (using the Samsung Simband) from the data. A subset of 4 audio, 1 text, and 5 physiologic features were identified using Sequential Forward Selection (SFS) for inclusion in a Neural Network (NN). ... We evaluated our model s performance using leave-one-subject-out crossvalidation and compared the performance to 20 baseline models and a NN with all features included in the input layer. |
| Researcher Affiliation | Academia | Tuka Al Hanai and Mohammad Mahdi Ghassemi* Massachusetts Institute of Technology, Cambridge MA 02139, USA EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper states 'We present a novel multi-modal dataset containing audio, physiologic, and text transcriptions from 31 narrative conversations.' but does not provide access information (link, DOI, or citation for public access). |
| Dataset Splits | Yes | To ensure the robustness of the identified features, the forward selection algorithm was performed on ten folds of our dataset (90% training, 10% validation) and a feature was marked for inclusion in our models only if it was selected in 5 or more of the folds. |
| Hardware Specification | No | The paper mentions data collection devices (Samsung Simband, Apple iPhone 5S) but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments or training the models. |
| Software Dependencies | No | The paper mentions software like 'open SMILE Toolkit' and 'Senti Word Net Lexicon' but does not specify their version numbers. |
| Experiment Setup | Yes | We optimized both the network topology, and the location of our selected features within the topology. More specifically, we trained all possible configurations of a NN with a number of hidden layers between 0 and 2 (where 0 hidden layers corresponds to a logistic regression). ... The optimal topology was a two hidden-layer network with six nodes in the first layer and three nodes in the second layer. |