4 citations · 12 across the 19 of their papers we have counts for
19 papers
Source-Adaptive Data Curation for Bilingual NVV-Aware ASR
Yuang Cao, Qirui Zhan, Jingbin Hu +8
Nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, convey affective and interactional information that conventional automatic speech recognition (ASR) sy…
MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
Zheng Lian, Xiaojiang Peng, Kele Xu +16
MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent rese…
MINT-Bench: A Comprehensive Multilingual Benchmark for Instruction-Following Text-to-Speech
Huakang Chen, Jingbin Hu, Liumeng Xue +12
Instruction-following text-to-speech (TTS) has emerged as an important capability for controllable and expressive speech generation, yet its evaluation remains underdeveloped due t…
NVV-SuperBench: Beyond Words, Beyond Quality-Benchmarking Nonverbal Vocalizations in Speech Generation
Liumeng Xue, Weizhen Bian, Jiahao Pan +9
Nonverbal vocalizations (NVVs), such as laughing, sighing, and sobbing, are essential for human-like speech, yet standardized evaluation rarely jointly assesses whether systems gen…
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
Xinsheng Wang, Mingqi Jiang, Ziyang Ma +22
Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis. However, existing foundation models rely on multi-…
Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis
Zhen Ye, Xinfa Zhu, Chi-Min Chan +17
Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and i…
The ISCSLP 2024 Conversational Voice Clone (CoVoC) Challenge: Tasks, Results and Findings
Kangxiang Xia, Dake Guo, Jixun Yao +9
The ISCSLP 2024 Conversational Voice Clone (CoVoC) Challenge aims to benchmark and advance zero-shot spontaneous style voice cloning, particularly focusing on generating spontaneou…
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Linhan Ma, Dake Guo, Kun Song +7
With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we pr…
Text-aware and Context-aware Expressive Audiobook Speech Synthesis
Dake Guo, Xinfa Zhu, Liumeng Xue +3
Recent advances in text-to-speech have significantly improved the expressiveness of synthetic speech. However, a major challenge remains in generating speech that captures the dive…
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
Hanzhao Li, Liumeng Xue, Haohan Guo +6
The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction. To avoid t…
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Ruibin Yuan, Hanfeng Lin, Yi Wang +32
While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…
SponTTS: modeling and transferring spontaneous style for TTS
Hanzhao Li, Xinfa Zhu, Liumeng Xue +3
Spontaneous speaking style exhibits notable differences from other speaking styles due to various spontaneous phenomena (e.g., filled pauses, prolongation) and substantial prosody…
HiGNN-TTS: Hierarchical Prosody Modeling with Graph Neural Networks for Expressive Long-form TTS
Dake Guo, Xinfa Zhu, Liumeng Xue +4
Recent advances in text-to-speech, particularly those based on Graph Neural Networks (GNNs), have significantly improved the expressiveness of short-form synthetic speech. However,…
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Zhichao Wang, Liumeng Xue, Qiuqiang Kong +4
Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typ…
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Ziqian Ning, Qicong Xie, Pengcheng Zhu +5
Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To addr…
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Liumeng Xue, Frank K. Soong, Shaofei Zhang +1
Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to re…
Learning Noise-independent Speech Representation for High-quality Voice Conversion for Noisy Target Speakers
Liumeng Xue, Shan Yang, Na Hu +2
Building a voice conversion system for noisy target speakers, such as users providing noisy samples or Internet found data, is a challenging task since the use of contaminated spee…
Controllable Emotion Transfer For End-to-End Speech Synthesis
Tao Li, Shan Yang, Liumeng Xue +1
Emotion embedding space learned from references is a straightforward approach for emotion transfer in encoder-decoder structured emotional text to speech (TTS) systems. However, th…
Building a mixed-lingual neural TTS system with only monolingual data
Liumeng Xue, Wei Song, Guanghui Xu +2
When deploying a Chinese neural text-to-speech (TTS) synthesis system, one of the challenges is to synthesize Chinese utterances with English phrases or words embedded. This paper…