1 citations · 1 across the 4 of their papers we have counts for
4 papers
DubWise: Video-Guided Speech Duration Control in Multimodal LLM-based Text-to-Speech for Dubbing
Neha Sahipjohn, Ashishkumar Gudmalwar, Nirmesh Shah +2
Audio-visual alignment after dubbing is a challenging research problem. To this end, we propose a novel method, DubWise Multi-modal Large Language Model (LLM)-based Text-to-Speech…
VECL-TTS: Voice identity and Emotional style controllable Cross-Lingual Text-to-Speech
Ashishkumar Gudmalwar, Nirmesh Shah, Sai Akarsh +2
Despite the significant advancements in Text-to-Speech (TTS) systems, their full utilization in automatic dubbing remains limited. This task necessitates the extraction of voice id…
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning
Shivam Ratnakant Mhaskar, Nirmesh J. Shah, Mohammadi Zaki +3
Traditional Automatic Video Dubbing (AVD) pipeline consists of three key modules, namely, Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), and Text-to-Speech (…
Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing
Nirmesh Shah, Mayank Kumar Singh, Naoya Takahashi +1
Primary goal of an emotional voice conversion (EVC) system is to convert the emotion of a given speech signal from one style to another style without modifying the linguistic conte…