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20162025
most citedImproving Text-To-Audio Models with Synthetic Captions

1 citations · 3 across the 8 of their papers we have counts for

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cs.SD2025

Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding

Zhifeng Kong, Arushi Goel, Joao Felipe Santos +4

Chain-of-thought reasoning has demonstrated significant improvements in large language models and vision language models, yet its potential for audio language models remains largel…

cs.SD2024

Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment

Paarth Neekhara, Shehzeen Hussain, Subhankar Ghosh +4

Large Language Model (LLM) based text-to-speech (TTS) systems have demonstrated remarkable capabilities in handling large speech datasets and generating natural speech for new spea…

cs.SD2024

Scaling NVIDIA's Multi-speaker Multi-lingual TTS Systems with Zero-Shot TTS to Indic Languages

Akshit Arora, Rohan Badlani, Sungwon Kim +2

In this paper, we describe the TTS models developed by NVIDIA for the MMITS-VC (Multi-speaker, Multi-lingual Indic TTS with Voice Cloning) 2024 Challenge. In Tracks 1 and 2, we uti…

cs.SD2023

VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation

Rohan Badlani, Akshit Arora, Subhankar Ghosh +5

We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports ex…

cs.SD20231 cited

Multilingual Multiaccented Multispeaker TTS with RADTTS

Rohan Badlani, Rafael Valle, Kevin J. Shih +3

We work to create a multilingual speech synthesis system which can generate speech with the proper accent while retaining the characteristics of an individual voice. This is challe…

cs.SD20161 cited

ABROA : Audio-Based Room-Occupancy Analysis using Gaussian Mixtures and Hidden Markov Models

Rafael Valle

This paper outlines preliminary steps towards the development of an audio- based room-occupancy analysis model. Our approach borrows from speech recognition tradition and is based…