3 accepted papers
Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommender systems that are directly trained on user interaction data. The goal of this work is to remove designated interactions and their downstream influence while preserving recomme…
Recommender systems used in online platforms can drive users to consume content continuously in an attempt to maximize satisfaction. Such engagement is invariably broken due to more pressing work, alternate pursuits, distractions or fatigue. Recommender systems need to ensure the continuity of exper…
We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to disentangle features relating to affective prosody from those arising due…