3 accepted papers
Many natural language questions are inherently subjective. They can not be answered properly if we do not know the personal preferences of the answerer. For example, "Do you like cats?" There is no "the only correct answer" to this question. To answer it, the model has to be able to capture the pers…
We propose a real-time Convolutional Neural Network model for speech emotion detection. Our model is trained from raw audio on a small dataset of TED talks speech data, manually annotated into three emotion classes: “Angry”, “Happy” and “Sad”. It achieves an average accuracy of 66.1%, 5% higher than…
We propose a method to predict humor response in dialog using acoustic and language features. We use data from two popular TV sitcoms - "The Big Bang Theory" and "Seinfeld" - to predict how the audience responds to humor. Due to the sequentiality of humor response in dialogues we use a Conditional R…