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Found 7 papers, 0 papers with code
Empathic Machines: Using Intermediate Features as Levers to Emulate Emotions in Text-To-Speech Systems
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.
Estimation of individual causal effects in network setup for multiple treatments
To measure the representation loss, we extend existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from the binary treatment setting to the multiple treatments scenario.
MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting
We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech.
ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations
We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations.
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise
The approach incorporates insights and business rules from domain experts in the form of easily observable and specifiable constraints, which are used as weak supervision by a machine learning model.
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
It is an emotion detection task on dialogues in the EmotionLines dataset.
