12 citations · 51 across the 25 of their papers we have counts for
25 papers
Revisiting the Entropy Semiring for Neural Speech Recognition
Oscar Chang, Dongseong Hwang, Olivier Siohan
In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information be…
Edit Distance based RL for RNNT decoding
Dongseong Hwang, Changwan Ryu, Khe Chai Sim
RNN-T is currently considered the industry standard in ASR due to its exceptional WERs in various benchmark tests and its ability to support seamless streaming and longform transcr…
Modular Domain Adaptation for Conformer-Based Streaming ASR
Qiujia Li, Bo Li, Dongseong Hwang +2
Speech data from different domains has distinct acoustic and linguistic characteristics. It is common to train a single multidomain model such as a Conformer transducer for speech…
Efficient Domain Adaptation for Speech Foundation Models
Bo Li, Dongseong Hwang, Zhouyuan Huo +8
Foundation models (FMs), that are trained on broad data at scale and are adaptable to a wide range of downstream tasks, have brought large interest in the research community. Benef…
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion
Zhouyuan Huo, Khe Chai Sim, Bo Li +3
Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks…
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR
Dongseong Hwang, Khe Chai Sim, Yu Zhang +1
Knowledge distillation is an effective machine learning technique to transfer knowledge from a teacher model to a smaller student model, especially with unlabeled data. In this pap…