2 citations · 2 across the 5 of their papers we have counts for
5 papers
LLM can Read Spectrogram: Encoder-free Speech-Language Modeling
Ruchao Fan, Yiming Wang, Yuxuan Hu +6
Recent speech-aware large language models (Speech-LLMs) rely on pre-trained speech encoders to convert audio into semantic/acoustic rich representations consumable by LLM. In this…
Accelerating Flow-Matching-Based Text-to-Speech via Empirically Pruned Step Sampling
Qixi Zheng, Yushen Chen, Zhikang Niu +4
Flow-matching-based text-to-speech (TTS) models, such as Voicebox, E2 TTS, and F5-TTS, have attracted significant attention in recent years. These models require multiple sampling…
Revealing the Intrinsic Ethical Vulnerability of Aligned Large Language Models
Jiawei Lian, Jianhong Pan, Lefan Wang +3
Large language models (LLMs) are foundational explorations to artificial general intelligence, yet their alignment with human values via instruction tuning and preference learning…
TacoLM: GaTed Attention Equipped Codec Language Model are Efficient Zero-Shot Text to Speech Synthesizers
Yakun Song, Zhuo Chen, Xiaofei Wang +3
Neural codec language model (LM) has demonstrated strong capability in zero-shot text-to-speech (TTS) synthesis. However, the codec LM often suffers from limitations in inference s…
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
Yakun Song, Zhuo Chen, Xiaofei Wang +2
The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation. However, exi…