activity
20212023
most citedDualHSIC: HSIC-Bottleneck and Alignment for Continual Learning

4 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

eess.AS2023★ 1 cited

Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition

Shaoshi Ling, Yuxuan Hu, Shuangbei Qian +5

Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (…

eess.AS2023

Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text

Eric Sun, Jinyu Li, Jian Xue +1

In end-to-end automatic speech recognition system, one of the difficulties for language expansion is the limited paired speech and text training data. In this paper, we propose a n…

cs.LG2023★ 4 cited

DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning

Zifeng Wang, Zheng Zhan, Yifan Gong +4

Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of da…

cs.CL2023

Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training

Eric Sun, Jinyu Li, Yuxuan Hu +8

We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users dur…

eess.AS2022

Endpoint Detection for Streaming End-to-End Multi-talker ASR

Liang Lu, Jinyu Li, Yifan Gong

Streaming end-to-end multi-talker speech recognition aims at transcribing the overlapped speech from conversations or meetings with an all-neural model in a streaming fashion, whic…

cs.CL2021★ 1 cited

Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition

Zhong Meng, Yashesh Gaur, Naoyuki Kanda +4

Text-only adaptation of an end-to-end (E2E) model remains a challenging task for automatic speech recognition (ASR). Language model (LM) fusion-based approaches require an addition…

cs.LG2021

Diarisation using location tracking with agglomerative clustering

Jeremy H. M. Wong, Igor Abramovski, Xiong Xiao +1

Previous works have shown that spatial location information can be complementary to speaker embeddings for a speaker diarisation task. However, the models used often assume that sp…

cs.SD2021

Joint speaker diarisation and tracking in switching state-space model

Jeremy H. M. Wong, Yifan Gong

Speakers may move around while diarisation is being performed. When a microphone array is used, the instantaneous locations of where the sounds originated from can be estimated, an…