25 citations · 28 across the 10 of their papers we have counts for
10 papers
Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch
Stella Eva Tsiapali, Cong-Thanh Do, Kate Knill
Large language models (LLMs) achieve state-of-the-art (SOTA) performance across language tasks, but are costly to deploy due to their size and resource demands. Knowledge Distillat…
Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models
Cong-Thanh Do, Rama Doddipatla, Kate Knill
Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Dis…
WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding
Mohan Li, Cong-Thanh Do, Simon Keizer +3
Speech large language models (speech-LLMs) integrate speech and text-based foundation models to provide a unified framework for handling a wide range of downstream tasks. In this p…
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis
Cong-Thanh Do, Shuhei Imai, Rama Doddipatla +1
This paper investigates the use of unsupervised text-to-speech synthesis (TTS) as a data augmentation method to improve accented speech recognition. TTS systems are trained with a…
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer
Cong-Thanh Do, Mohan Li, Rama Doddipatla
This paper proposes a new approach to perform unsupervised fine-tuning and self-training using unlabeled speech data for recurrent neural network (RNN)-Transducer (RNN-T) end-to-en…
Robust multi-sensor Generalized Labeled Multi-Bernoulli filter
Cong-Thanh Do, Tran Thien Dat Nguyen, Hoa Van Nguyen
This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sens…
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition
Cong-Thanh Do, Rama Doddipatla, Thomas Hain
This paper proposes an adaptation method for end-to-end speech recognition. In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the comp…
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Shucong Zhang, Cong-Thanh Do, Rama Doddipatla +3
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in gen…
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
Cong-Thanh Do, Tran Thien Dat Nguyen, Diluka Moratuwage +2
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on loc…
End-to-End Speech Recognition with High-Frame-Rate Features Extraction
Cong-Thanh Do
State-of-the-art end-to-end automatic speech recognition (ASR) extracts acoustic features from input speech signal every 10 ms which corresponds to a frame rate of 100 frames/secon…