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Research

Investigating Semantic Roles for Emotion Role Prediction

A little detailed scientific report on this project is available on Researchgate. Please feel free to read and share your feedback with me over email

Emotion analysis primarily focuses on classifying, predicting and retrieving emotions and their related properties from text. However, only few research was conducted towards analyzing the semantic roles of emotions, i.e. who is experiencing which emotion, what caused it and what or whom is it directed towards. This project investigate the influence of semantic role labels on emotion role prediction. Building on top of previous approaches and resources, I've implemented a framework for predicting emotion roles using different features with co-researcher Maximilian Wegge. We find that semantic role label features have no significant influence on the task and identify two possible reasons for that. emotion_roles

Denoising diffusion for speech enhancement

This research has been made available on Researchgate. You can also send me a request over personal email to access a pdf copy of the report if you encounter some issues with the link above.

Introduction

Audio recordings in the real world are typically tainted by noise and other distortions. These distortions come from many factors such as environmental noises, and distortions from various kinds of electronics, circuits, and microphones. These noises and distortions cause issues during the perception of speech to the receiver. This project tries to provide a solution to the problem of noise in speech using a generative approach and compares the effectiveness of the generative approach over other existing approaches.