22 citations · 41 across the 8 of their papers we have counts for
8 papers
Improving Text-To-Audio Models with Synthetic Captions
Zhifeng Kong, Sang-gil Lee, Deepanway Ghosal +5
It is an open challenge to obtain high quality training data, especially captions, for text-to-audio models. Although prior methods have leveraged \textit{text-only language models…
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation
Yingting Li, Ambuj Mehrish, Bryan Chew +2
Different languages have distinct phonetic systems and vary in their prosodic features making it challenging to develop a Text-to-Speech (TTS) model that can effectively synthesise…
HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks
Yingting Li, Rishabh Bhardwaj, Ambuj Mehrish +2
Neural speech synthesis, or text-to-speech (TTS), aims to transform a signal from the text domain to the speech domain. While developing TTS architectures that train and test on th…
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
Xiang Li, Fan Bu, Ambuj Mehrish +4
Neural Text-to-Speech (TTS) systems find broad applications in voice assistants, e-learning, and audiobook creation. The pursuit of modern models, like Diffusion Models (DMs), hold…
A Review of Deep Learning Techniques for Speech Processing
Ambuj Mehrish, Navonil Majumder, Rishabh Bhardwaj +2
The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capab…
ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation
Ambuj Mehrish, Abhinav Ramesh Kashyap, Li Yingting +2
There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-repres…