Predicting Latent Narrative Mood Using Audio and Physiologic Data
Authors: Tuka AlHanai, Mohammad Ghassemi
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 tuka@mit.edu, ghassemi@mit.edu |
| 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. |