6 citations · 7 across the 18 of their papers we have counts for
20 papers
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…
ISA-Bench: Benchmarking Instruction Sensitivity for Large Audio Language Models
Bohan Li, Wenbin Huang, Yuhang Qiu +7
Large Audio Language Models (LALMs), which couple acoustic perception with large language models (LLMs) to extract and understand diverse information from audio, have attracted int…
AHAMask: Reliable Task Specification for Large Audio Language Models without Instructions
Yiwei Guo, Bohan Li, Hankun Wang +4
Although current large audio language models (LALMs) extend text large language models (LLMs) with generic acoustic understanding abilities, they usually suffer from prompt sensiti…
Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Bohan Li, Zhihan Li, Haoran Wang +5
Recently, autoregressive (AR) language models have emerged as a dominant approach in speech synthesis, offering expressive generation and scalable training. However, conventional A…
CodecSlime: Temporal Redundancy Compression of Neural Speech Codec via Dynamic Frame Rate
Hankun Wang, Yiwei Guo, Chongtian Shao +2
Neural speech codecs have been widely used in audio compression and various downstream tasks. Current mainstream codecs are fixed-frame-rate (FFR), which allocate the same number o…
Towards General Discrete Speech Codec for Complex Acoustic Environments: A Study of Reconstruction and Downstream Task Consistency
Haoran Wang, Guanyu Chen, Bohan Li +5
Neural speech codecs excel in reconstructing clean speech signals; however, their efficacy in complex acoustic environments and downstream signal processing tasks remains underexpl…
Recent Advances in Discrete Speech Tokens: A Review
Yiwei Guo, Zhihan Li, Hankun Wang +7
The rapid advancement of speech generation technologies in the era of large language models (LLMs) has established discrete speech tokens as a foundational paradigm for speech repr…
AdaEAGLE: Optimizing Speculative Decoding via Explicit Modeling of Adaptive Draft Structures
Situo Zhang, Hankun Wang, Da Ma +4
Speculative Decoding (SD) is a popular lossless technique for accelerating the inference of Large Language Models (LLMs). We show that the decoding speed of SD frameworks with stat…
Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding
Bohan Li, Hankun Wang, Situo Zhang +2
The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenge…
A Survey on Speech Large Language Models for Understanding
Jing Peng, Yucheng Wang, Bohan Li +9
Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that…
LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec
Yiwei Guo, Zhihan Li, Chenpeng Du +3
Although discrete speech tokens have exhibited strong potential for language model-based speech generation, their high bitrates and redundant timbre information restrict the develo…
vec2wav 2.0: Advancing Voice Conversion via Discrete Token Vocoders
Yiwei Guo, Zhihan Li, Junjie Li +5
We propose a new speech discrete token vocoder, vec2wav 2.0, which advances voice conversion (VC). We use discrete tokens from speech self-supervised models as the content features…
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge
Yiwei Guo, Chenrun Wang, Yifan Yang +9
Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice sy…
Attention-Constrained Inference for Robust Decoder-Only Text-to-Speech
Hankun Wang, Chenpeng Du, Yiwei Guo +3
Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and re…