23 citations · 83 across the 20 of their papers we have counts for
6 papers · 1 filter
MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data
Subhankar Ghosh, Jason Li, Paarth Neekhara +4
Neural Text-to-Speech (TTS) systems achieve remarkable quality on short utterances but long-form speech generation shows prosodic drift, speaker inconsistencies and sentence bounda…
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance
Shehzeen Hussain, Paarth Neekhara, Xuesong Yang +6
While autoregressive speech token generation models produce speech with remarkable variety and naturalness, their inherent lack of controllability often results in issues such as h…
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…
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
Paarth Neekhara, Shehzeen Hussain, Rafael Valle +5
We propose SelfVC, a training strategy to iteratively improve a voice conversion model with self-synthesized examples. Previous efforts on voice conversion focus on factorizing spe…
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…
Expressive Neural Voice Cloning
Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov +2
Voice cloning is the task of learning to synthesize the voice of an unseen speaker from a few samples. While current voice cloning methods achieve promising results in Text-to-Spee…