Search Results for author:
Found 9 papers, 1 papers with code
Deep CLAS: Deep Contextual Listen, Attend and Spell
In this work, we propose deep CLAS to use contextual information better.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Lightweight Transducer Based on Frame-Level Criterion
The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix.
Ranked #5 on
Speech Recognition
on AISHELL-1
The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge.
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge
This technical report details our submission system to the CHiME-7 DASR Challenge, which focuses on speaker diarization and speech recognition under complex multi-speaker scenarios.
Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation
In this paper, we propose an adaptive two-stage knowledge distillation method consisting of hidden layer learning and output layer learning.
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information
Multilingual end-to-end models have shown great improvement over monolingual systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit
Speech pre-training has shown great success in learning useful and general latent representations from large-scale unlabeled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition
Self-supervised learning (SSL) models have achieved considerable improvements in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation
AV2vec has a student and a teacher module, in which the student performs a masked latent feature regression task using the multimodal target features generated online by the teacher.
