35 citations · 40 across the 7 of their papers we have counts for
7 papers
Continual Speech Learning with Fused Speech Features
Guitao Wang, Jinming Zhao, Hao Yang +3
Rapid growth in speech data demands adaptive models, as traditional static methods fail to keep pace with dynamic and diverse speech information. We introduce continuous speech lea…
Scaling A Simple Approach to Zero-Shot Speech Recognition
Jinming Zhao, Vineel Pratap, Michael Auli
Despite rapid progress in increasing the language coverage of automatic speech recognition, the field is still far from covering all languages with a known writing script. Recent w…
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Zheng Lian, Haiyang Sun, Licai Sun +15
Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dat…
Double Mixture: Towards Continual Event Detection from Speech
Jingqi Kang, Tongtong Wu, Jinming Zhao +6
Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. Traditional ASR systems often overlook the interplay between…
Towards Event Extraction from Speech with Contextual Clues
Jingqi Kang, Tongtong Wu, Jinming Zhao +4
While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-…
Simultaneous Machine Translation with Large Language Models
Minghan Wang, Jinming Zhao, Thuy-Trang Vu +3
Real-world simultaneous machine translation (SimulMT) systems face more challenges than just the quality-latency trade-off. They also need to address issues related to robustness w…
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Jinming Zhao, Ming Liu, Longxiang Gao +5
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this pa…