14 citations · 20 across the 14 of their papers we have counts for
14 papers
SonicWeave: Chunk-Routed Mixture-of-Experts for Unified Audio Scene Generation
Yunrui Cai, Xu Li, Yucheng Zhou +8
Text-conditioned general audio generation is moving beyond isolated speech, music, and sound-effect synthesis toward a single model that can compose them into controllable, coheren…
UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment
Yuanyuan Wang, Dongchao Yang, Yayue Deng +4
Evaluating speech generation still relies heavily on human judgments, such as Mean Opinion Score (MOS), which are expensive, subjective, and difficult to reproduce at scale. While…
DualSpeechLM: Towards Unified Speech Understanding and Generation via Dual Speech Token Modeling with Large Language Models
Yuanyuan Wang, Dongchao Yang, Yiwen Shao +5
Extending pre-trained text Large Language Models (LLMs)'s speech understanding or generation abilities by introducing various effective speech tokens has attracted great attention…
Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models
Weiqin Li, Peiji Yang, Yicheng Zhong +5
Spontaneous style speech synthesis, which aims to generate human-like speech, often encounters challenges due to the scarcity of high-quality data and limitations in model capabili…
Autoregressive Speech Synthesis without Vector Quantization
Lingwei Meng, Long Zhou, Shujie Liu +9
We present MELLE, a novel continuous-valued token based language modeling approach for text-to-speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram f…
Neural Concatenative Singing Voice Conversion: Rethinking Concatenation-Based Approach for One-Shot Singing Voice Conversion
Binzhu Sha, Xu Li, Zhiyong Wu +2
Any-to-any singing voice conversion (SVC) is confronted with the challenge of ``timbre leakage'' issue caused by inadequate disentanglement between the content and the speaker timb…
Cross-Speaker Encoding Network for Multi-Talker Speech Recognition
Jiawen Kang, Lingwei Meng, Mingyu Cui +4
End-to-end multi-talker speech recognition has garnered great interest as an effective approach to directly transcribe overlapped speech from multiple speakers. Current methods typ…
StyleSpeech: Self-supervised Style Enhancing with VQ-VAE-based Pre-training for Expressive Audiobook Speech Synthesis
Xueyuan Chen, Xi Wang, Shaofei Zhang +4
The expressive quality of synthesized speech for audiobooks is limited by generalized model architecture and unbalanced style distribution in the training data. To address these is…
SimCalib: Graph Neural Network Calibration based on Similarity between Nodes
Boshi Tang, Zhiyong Wu, Xixin Wu +4
Graph neural networks (GNNs) have exhibited impressive performance in modeling graph data as exemplified in various applications. Recently, the GNN calibration problem has attracte…
Injecting linguistic knowledge into BERT for Dialogue State Tracking
Xiaohan Feng, Xixin Wu, Helen Meng
Dialogue State Tracking (DST) models often employ intricate neural network architectures, necessitating substantial training data, and their inference process lacks transparency. T…
UniAudio: An Audio Foundation Model Toward Universal Audio Generation
Dongchao Yang, Jinchuan Tian, Xu Tan +9
Large Language models (LLM) have demonstrated the capability to handle a variety of generative tasks. This paper presents the UniAudio system, which, unlike prior task-specific app…
Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts
Shun Lei, Yixuan Zhou, Liyang Chen +8
Zero-shot text-to-speech (TTS) synthesis aims to clone any unseen speaker's voice without adaptation parameters. By quantizing speech waveform into discrete acoustic tokens and mod…
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
Tianhua Zhang, Jiaxin Ge, Hongyin Luo +7
How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning? We propose natural language embedded programs (NLE…
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning
Haohan Guo, Fenglong Xie, Jiawen Kang +3
This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Repres…