Search Results for author:
Found 7 papers, 1 papers with code
Bridging the Gap: Deciphering Tabular Data Using Large Language Model
This research marks the first application of large language models to table-based question answering tasks, enhancing the model's comprehension of both table structures and content.
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition
During inference, an error-based alignment sampling method is investigated in depth to reduce the alignment mismatch in the training and testing processes.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment
Automatic pronunciation assessment is an important technology to help self-directed language learners.
Ranked #2 on
Phone-level pronunciation scoring
on speechocean762
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition
For the analyses, we plot attention weight distributions in the decoders to visualize the relationships between token-level acoustic embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CASS-NAT: CTC Alignment-based Single Step Non-autoregressive Transformer for Speech Recognition
The information are used to extract acoustic representation for each token in parallel, referred to as token-level acoustic embedding which substitutes the word embedding in autoregressive transformer (AT) to achieve parallel generation in decoder.
Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation
This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task.
Robotics
