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Found 7 papers, 2 papers with code
Persistence Homology of TEDtalk: Do Sentence Embeddings Have a Topological Shape?
From our results, we could not conclude that the topological shapes of the sentence embeddings can help us train a better model for public speaking rating.
Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches
Our work ties together a novel metric for public speeches in both verbal and non-verbal domain with the computational power of a neural network to design a fair prediction system for speakers.
FairyTED: A Fair Rating Predictor for TED Talk Data
This causal model contributes in generating counterfactual data to train a fair predictive model.
A Causality-Guided Prediction of the TED Talk Ratings from the Speech-Transcripts using Neural Networks
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills.
Predicting TED Talk Ratings from Language and Prosody
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers.
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community.
SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology
This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product.
