17 citations · 43 across the 7 of their papers we have counts for
8 papers
WST: Weakly Supervised Transducer for Automatic Speech Recognition
Dongji Gao, Chenda Liao, Changliang Liu +5
The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality anno…
Deploying self-supervised learning in the wild for hybrid automatic speech recognition
Mostafa Karimi, Changliang Liu, Kenichi Kumatani +3
Self-supervised learning (SSL) methods have proven to be very successful in automatic speech recognition (ASR). These great improvements have been reported mostly based on highly c…
Exploring Transformers for Large-Scale Speech Recognition
Liang Lu, Changliang Liu, Jinyu Li +1
While recurrent neural networks still largely define state-of-the-art speech recognition systems, the Transformer network has been proven to be a competitive alternative, especiall…
L-Vector: Neural Label Embedding for Domain Adaptation
Zhong Meng, Hu Hu, Jinyu Li +4
We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target dom…
Advances in Online Audio-Visual Meeting Transcription
Takuya Yoshioka, Igor Abramovski, Cem Aksoylar +23
This paper describes a system that generates speaker-annotated transcripts of meetings by using a microphone array and a 360-degree camera. The hallmark of the system is its abilit…
Low-Latency Speaker-Independent Continuous Speech Separation
Takuya Yoshioka, Zhuo Chen, Changliang Liu +3
Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed…
Layer Trajectory LSTM
Jinyu Li, Changliang Liu, Yifan Gong
It is popular to stack LSTM layers to get better modeling power, especially when large amount of training data is available. However, an LSTM-RNN with too many vanilla LSTM layers…
Developing Far-Field Speaker System Via Teacher-Student Learning
Jinyu Li, Rui Zhao, Zhuo Chen +4
In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt…