18 citations · 92 across the 29 of their papers we have counts for
7 papers · 1 filter
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
Spoken Function Calling: A New Perspective on Spoken Language Understanding for Large Audio Language Models
Yuezhang Peng, Yuxin Liu, Changfeng Gao +4
Spoken Language Understanding (SLU) is the core component of task-oriented dialogue systems and a pivotal link in achieving seamless human-agent interaction. While traditional SLU…
Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition
Peng Wang, Yanqiao Zhu, Zixuan Jiang +8
Recent years have witnessed remarkable progress in automatic speech recognition (ASR), driven by advances in model architectures and large-scale training data. However, two importa…
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Qian Chen, Yafeng Chen, Yanni Chen +33
Recent advancements in large language models (LLMs) and multimodal speech-text models have laid the groundwork for seamless voice interactions, enabling real-time, natural, and hum…
An Embarrassingly Simple Approach for LLM with Strong ASR Capacity
Ziyang Ma, Guanrou Yang, Yifan Yang +8
In this paper, we focus on solving one of the most important tasks in the field of speech processing, i.e., automatic speech recognition (ASR), with speech foundation encoders and…
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
Ziyang Ma, Zhisheng Zheng, Jiaxin Ye +4
We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillatio…