7 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…
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…
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…
NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
Edresson Casanova, Paarth Neekhara, Ryan Langman +6
Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling…
SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model
Ke Hu, Ehsan Hosseini-Asl, Chen Chen +7
Spoken dialogue is an intuitive form of human-computer interaction, yet current speech language models often remain constrained to turn-based exchanges, lacking real-time adaptabil…
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…