no code implementations • NAACL (DeeLIO) 2021 • Vivek Srivastava, Stephen Pilli, Savita Bhat, Niranjan Pedanekar, Shirish Karande
In the era of digital marketing, both brand managers and consumers engage with a vast amount of digital marketing content.
no code implementations • NAACL 2022 • Saiteja Kosgi, Sarath Sivaprasad, Niranjan Pedanekar, Anil Nelakanti, Vineet Gandhi
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.
no code implementations • 28 Nov 2024 • Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar
In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images.
no code implementations • 18 Dec 2023 • Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar, Naoyuki Onoe
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.
no code implementations • 19 May 2023 • Neil Shah, Vishal Tambrahalli, Saiteja Kosgi, Niranjan Pedanekar, Vineet Gandhi
We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech.
no code implementations • 1 Mar 2023 • Neil Shah, Saiteja Kosgi, Vishal Tambrahalli, Neha Sahipjohn, Niranjan Pedanekar, Vineet Gandhi
We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations.
no code implementations • 18 Mar 2022 • Ragja Palakkadavath, Sarath Sivaprasad, Shirish Karande, Niranjan Pedanekar
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.
no code implementations • WS 2018 • Rohit Saxena, Savita Bhat, Niranjan Pedanekar
It is an emotion detection task on dialogues in the EmotionLines dataset.