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NormToken: Speaker- and Duration-Normalized Semantic Speech Tokens via Iterative S2U-T2U Refinement
Hanlin Zhang, Daxin Tan, Dehua Tao +3
Semantic speech tokens should preserve linguistic content while suppressing utterance-specific acoustic and duration variation. However, existing speech-to-unit (S2U) tokenizers of…
SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing
Hanlin Zhang, Daxin Tan, Dehua Tao +3
Instruction-guided speech editing requires a model to modify specified speech attributes while preserving non-target characteristics. Despite rapid progress in Speech Large Languag…
Speech-Omni-Lite: Portable Speech Interfaces for Vision-Language Models
Dehua Tao, Xuan Luo, Daxin Tan +5
While large-scale omni-models have demonstrated impressive capabilities across various modalities, their strong performance heavily relies on massive multimodal data and incurs sub…
PROST-LLM: Progressively Enhancing the Speech-to-Speech Translation Capability in LLMs
Jing Xu, Jiaqi Wang, Daxin Tan +1
Although Large Language Models (LLMs) excel in many tasks, their application to Speech-to-Speech Translation (S2ST) is underexplored and hindered by data scarcity. To bridge this g…
DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion
Hanlin Zhang, Daxin Tan, Dehua Tao +5
Speech tokenizers are a key building block of fully discrete Speech LLMs. Existing tokenizers either prioritize semantic encoding, fuse semantic content with acoustic style insepar…