996 citations · 2.4k across the 62 of their papers we have counts for
6 papers · 1 filter
Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio
Yang Zhang, Krishna C. Puvvada, Vitaly Lavrukhin +1
We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR). The model consist…
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…
ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations
Shehzeen Hussain, Paarth Neekhara, Jocelyn Huang +2
In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. First, we develop a multi-task model to decompose a…
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
Somshubra Majumdar, Shantanu Acharya, Vitaly Lavrukhin +1
Automatic speech recognition models are often adapted to improve their accuracy in a new domain. A potential drawback of model adaptation to new domains is catastrophic forgetting,…
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
Sang-gil Lee, Wei Ping, Boris Ginsburg +2
Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthe…
Adapting TTS models For New Speakers using Transfer Learning
Paarth Neekhara, Jason Li, Boris Ginsburg
Training neural text-to-speech (TTS) models for a new speaker typically requires several hours of high quality speech data. Prior works on voice cloning attempt to address this cha…