387 citations · 659 across the 12 of their papers we have counts for
12 papers
PACE: A Playback-Aligned Context Engine for LLM-Based Full-Duplex Voice Dialogue
Shibo Wang, Zicheng Zhang, Libo Wang +1
LLM-based full-duplex voice services allow users to speak while the assistant is responding. Because servers can generate output and advance dialogue state faster than clients can…
UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities
Qi Jia, Haodong Zhao, Dun Pei +7
Benchmarking large language models (LLMs) and agents in multi-turn interactive scenarios is essential for understanding their practical capabilities. However, existing evaluation p…
LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks
Hengjian Gao, Kaiwei Zhang, Shibo Wang +8
The rapid progress of Multimodal Large Language Models (MLLMs) marks a significant step toward artificial general intelligence, offering great potential for augmenting human capabi…
MMED: A Multimodal Micro-Expression Dataset based on Audio-Visual Fusion
Junbo Wang, Yan Zhao, Shuo Li +3
Micro-expressions (MEs) are crucial leakages of concealed emotion, yet their study has been constrained by a reliance on silent, visual-only data. To solve this issue, we introduce…
A Study on Regularization-Based Continual Learning Methods for Indic ASR
Gokul Adethya T, S. Jaya Nirmala
Indias linguistic diversity poses significant challenges for developing inclusive Automatic Speech Recognition (ASR) systems. Traditional multilingual models, which require simulta…
N-Grammer: Augmenting Transformers with latent n-grams
Aurko Roy, Rohan Anil, Guangda Lai +13
Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment i…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
GSPMD: General and Scalable Parallelization for ML Computation Graphs
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen +13
We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computations. It allows users to write programs in the same way as for a single de…
Exploring the limits of Concurrency in ML Training on Google TPUs
Sameer Kumar, James Bradbury, Cliff Young +16
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.…
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati, James Qin, Chung-Cheng Chiu +8
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (…
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen +3
In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update…
Scale MLPerf-0.6 models on Google TPU-v3 Pods
Sameer Kumar, Victor Bitorff, Dehao Chen +9
The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defin…