5 papers
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…
Align2Speak: Improving TTS for Low Resource Languages via ASR-Guided Online Preference Optimization
Shehzeen Hussain, Paarth Neekhara, Xuesong Yang +7
Developing high-quality text-to-speech (TTS) systems for low-resource languages is challenging due to the scarcity of paired text and speech data. In contrast, automatic speech rec…
Frame-Stacked Local Transformers For Efficient Multi-Codebook Speech Generation
Roy Fejgin, Paarth Neekhara, Xuesong Yang +6
Speech generation models based on large language models (LLMs) typically operate on discrete acoustic codes, which differ fundamentally from text tokens due to their multicodebook…
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…
Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation
Gerard I. Gállego, Roy Fejgin, Chunghsin Yeh +2
Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplif…