16 citations · 16 across the 8 of their papers we have counts for
8 papers
Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Siyin Wang, Zengrui Jin, Changli Tang +26
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Zhihong Lei, Xingyu Na, Mingbin Xu +5
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a…
Enhancing CTC-based speech recognition with diverse modeling units
Shiyi Han, Zhihong Lei, Mingbin Xu +2
In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transf…
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
Mingbin Xu, Alex Jin, Sicheng Wang +8
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the c…
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Zhihong Lei, Ernest Pusateri, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as c…
Acoustic Model Fusion for End-to-end Speech Recognition
Zhihong Lei, Mingbin Xu, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implic…
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Mingbin Xu, Congzheng Song, Ye Tian +10
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our…
Neural Diffusion Model for Microscopic Cascade Prediction
Cheng Yang, Maosong Sun, Haoran Liu +3
The prediction of information diffusion or cascade has attracted much attention over the last decade. Most cascade prediction works target on predicting cascade-level macroscopic p…