8 citations · 8 across the 12 of their papers we have counts for
15 papers
What Matters in Preference Post-Training of Continuous Autoregressive Speech Language Models
Yingda Shen, Yao Qian, Dekun Chen +6
Continuous autoregressive speech language models combine an autoregressive (AR) backbone with a diffusion or flow-matching head. Preference post-training requires choosing how to c…
AuraSE: Low-Hallucination Generative Speech Enhancement via Multimodal Flow Matching and Inference Policy Optimization
Yingda Shen, Yao Qian, Yuxuan Hu +7
Generative speech enhancement models can produce cleaner and more natural-sounding speech than conventional discriminative approaches, but may hallucinate by changing speech conten…
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…
Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation
Yuxuan Hu, Heng Lu, Ruchao Fan +8
Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can…
FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates
Jiaqi Li, Yao Qian, Yuxuan Hu +7
Neural audio codecs are foundational to speech language models. It is expected to have a low frame rate and decoupled semantic and acoustic information. A lower frame rate codec ca…
Next Tokens Denoising for Speech Synthesis
Yanqing Liu, Ruiqing Xue, Chong Zhang +7
While diffusion and autoregressive (AR) models have significantly advanced generative modeling, they each present distinct limitations. AR models, which rely on causal attention, c…
Audio-Aware Large Language Models as Judges for Speaking Styles
Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin +8
Audio-aware large language models (ALLMs) can understand the textual and non-textual information in the audio input. In this paper, we explore using ALLMs as an automatic judge to…
Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Haibin Wu, Yuxuan Hu, Ruchao Fan +8
Speech language models (Speech LMs) enable end-to-end speech-text modeling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-t…
SLM-S2ST: A multimodal language model for direct speech-to-speech translation
Yuxuan Hu, Haibin Wu, Ruchao Fan +4
Speech-aware language models (LMs) have demonstrated capabilities in understanding spoken language while generating text-based responses. However, enabling them to produce speech o…
CoVoMix2: Advancing Zero-Shot Dialogue Generation with Fully Non-Autoregressive Flow Matching
Leying Zhang, Yao Qian, Xiaofei Wang +8
Generating natural-sounding, multi-speaker dialogue is crucial for applications such as podcast creation, virtual agents, and multimedia content generation. However, existing syste…
Isochrony-Controlled Speech-to-Text Translation: A study on translating from Sino-Tibetan to Indo-European Languages
Midia Yousefi, Yao Qian, Junkun Chen +5
End-to-end speech translation (ST), which translates source language speech directly into target language text, has garnered significant attention in recent years. Many ST applicat…
Investigating Neural Audio Codecs for Speech Language Model-Based Speech Generation
Jiaqi Li, Dongmei Wang, Xiaofei Wang +13
Neural audio codec tokens serve as the fundamental building blocks for speech language model (SLM)-based speech generation. However, there is no systematic understanding on how the…
VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers
Sanyuan Chen, Shujie Liu, Long Zhou +6
This paper introduces VALL-E 2, the latest advancement in neural codec language models that marks a milestone in zero-shot text-to-speech synthesis (TTS), achieving human parity fo…
CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations
Leying Zhang, Yao Qian, Long Zhou +9
Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along w…
Optimizing Alignment of Speech and Language Latent Spaces for End-to-End Speech Recognition and Understanding
Wei Wang, Shuo Ren, Yao Qian +4
The advances in attention-based encoder-decoder (AED) networks have brought great progress to end-to-end (E2E) automatic speech recognition (ASR). One way to further improve the pe…