6 citations · 7 across the 18 of their papers we have counts for
7 papers · 1 filter
dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model
Hankun Wang, Bohan Li, Shi Lian +7
Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface fo…
Read What You Hear: Reference-Free Hypotheses Evaluation with Acoustic Discrepancy
Zhihan Li, Hankun Wang, Yiwei Guo +3
Automatic speech recognition systems commonly rely on reference transcriptions for evaluation, while reference-free approaches often depend on internal confidence estimation or aux…
dots.tts Technical Report
Shi Lian, Changtao Li, Bohan Li +6
We present dotstts, a 2B-parameter continuous autoregressive text-to-speech (TTS) foundation model that models speech in a continuous latent space. Compared with existing contin…
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios
Changhao Pan, Rui Yang, Han Wang +12
Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A compre…
HoliTok: A Continuous Holistic Tokenization with Robust Dual Capabilities of Speech Generation and Understanding
Bohan Li, Shi Lian, Hankun Wang +6
Unified speech foundation models require a holistic tokenization space that is both learnable by language models and decodable into high-quality waveforms. Existing speech tokenize…
RAS: a Reliability Oriented Metric for Automatic Speech Recognition
Wenbin Huang, Yuhang Qiu, Bohan Li +5
Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading for both users and downstream…
PACER: Blockwise Pre-verification for Speculative Decoding with Adaptive Length
Situo Zhang, Yifan Zhang, Zichen Zhu +5
Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs) without sacrificing accuracy. Typically, SD employs a small…