5 papers
TASLA: Text-Aligned Speech Tokens with Multiple Layer-Aggregation
Ming-Hao Hsu, Liang-Hsuan Tseng, Hung-yi Lee +1
We propose Text-Aligned Speech Tokens with Multiple Layer-Aggregation (TASLA), which is a text-aligned speech tokenization framework that aims to address the problem that under a l…
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
Liang-Hsuan Tseng, Yi-Chang Chen, Kuan-Yi Lee +2
Recent efforts target spoken language models (SLMs) that not only listen but also speak for more natural human-LLM interaction. Joint speech-text modeling is a promising direction…
LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play
Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai +3
Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended ques…
Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generations
Cheng-Han Chiang, Hung-yi Lee
Long-form generations from large language models (LLMs) contain a mix of factual and non-factual claims, making evaluating factuality difficult. Prior works evaluate the factuality…
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
Liang-Hsuan Tseng, En-Pei Hu, Cheng-Han Chiang +4
Unsupervised automatic speech recognition (ASR) aims to learn the mapping between the speech signal and its corresponding textual transcription without the supervision of paired sp…