67 citations · 183 across the 27 of their papers we have counts for
27 papers
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Han Zhu, Dongji Gao, Gaofeng Cheng +3
When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, p…
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture
Haoran Miao, Gaofeng Cheng, Pengyuan Zhang +1
Recently, there has been increasing progress in end-to-end automatic speech recognition (ASR) architecture, which transcribes speech to text without any pre-trained alignments. One…
ForkNet: Simultaneous Time and Time-Frequency Domain Modeling for Speech Enhancement
Feng Dang, Qi Hu, Pengyuan Zhang +1
Previous research in speech enhancement has mostly focused on modeling time or time-frequency domain information alone, with little consideration given to the potential benefits of…
Speech Corpora Divergence Based Unsupervised Data Selection for ASR
Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang +1
Selecting application scenarios matching data is important for the automatic speech recognition (ASR) training, but it is difficult to measure the matching degree of the training c…
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge
Shuhao Deng, Chengfei Li, Jinfeng Bai +6
Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomeno…
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines
Gaofeng Cheng, Yifan Chen, Runyan Yang +9
The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a c…
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies
Zehan Li, Haoran Miao, Keqi Deng +4
There is often a trade-off between performance and latency in streaming automatic speech recognition (ASR). Traditional methods such as look-ahead and chunk-based methods, usually…
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
Yifan Chen, Yifan Guo, Qingxuan Li +3
For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible. Moreover, in most existing clustering-based methods, the tra…
Decoupled Federated Learning for ASR with Non-IID Data
Han Zhu, Jindong Wang, Gaofeng Cheng +2
Automatic speech recognition (ASR) with federated learning (FL) makes it possible to leverage data from multiple clients without compromising privacy. The quality of FL-based ASR c…
Boosting Cross-Domain Speech Recognition with Self-Supervision
Han Zhu, Gaofeng Cheng, Jindong Wang +3
The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions. Since the target domai…
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
Zehui Yang, Yifan Chen, Lei Luo +9
This paper introduces a high-quality rich annotated Mandarin conversational (RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours of conversatio…
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable
Ruiliu Fu, Han Wang, Xuejun Zhang +2
Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine reasoning pr…
Reminding the Incremental Language Model via Data-Free Self-Distillation
Han Wang, Ruiliu Fu, Chengzhang Li +3
Incremental language learning with pseudo-data can alleviate catastrophic forgetting in neural networks. However, to obtain better performance, former methods have higher demands f…
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
Han Zhu, Li Wang, Jindong Wang +3
Self-supervised pre-training could effectively improve the performance of low-resource automatic speech recognition (ASR). However, existing self-supervised pre-training are task-a…
A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio
Jianju Gu, Longbiao Cheng, Dingding Yao +2
This letter proposes a new blind source separation (BSS) framework termed minimum variance independent component analysis (MVICA), which can potentially achieve the maximum output…
The HCCL Speaker Verification System for Far-Field Speaker Verification Challenge
Zhuo Li, Ce Fang, Runqiu Xiao +3
This paper describes the systems submitted by team HCCL to the Far-Field Speaker Verification Challenge. Our previous work in the AIshell Speaker Verification Challenge 2019 shows…
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search
Yukun Liu, Ta Li, Pengyuan Zhang +1
Recently neural architecture search(NAS) has been successfully used in image classification, natural language processing, and automatic speech recognition(ASR) tasks for finding th…
Multi-Accent Adaptation based on Gate Mechanism
Han Zhu, Li Wang, Pengyuan Zhang +1
When only a limited amount of accented speech data is available, to promote multi-accent speech recognition performance, the conventional approach is accent-specific adaptation, wh…
A Model Compression Method with Matrix Product Operators for Speech Enhancement
Xingwei Sun, Ze-Feng Gao, Zhong-Yi Lu +2
The deep neural network (DNN) based speech enhancement approaches have achieved promising performance. However, the number of parameters involved in these methods is usually enormo…
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture
Haoran Miao, Gaofeng Cheng, Changfeng Gao +2
Recently, Transformer has gained success in automatic speech recognition (ASR) field. However, it is challenging to deploy a Transformer-based end-to-end (E2E) model for online spe…
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model
Hangting Chen, Pengyuan Zhang, Yonghong Yan
In this letter, we present a novel multi-talker minimum variance distortionless response (MVDR) beamforming as the front-end of an automatic speech recognition (ASR) system in a di…
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
Hangting Chen, Zuozhen Liu, Zongming Liu +2
This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge. Our acoustic scene classification (ASC) system adopts a data augmentation scheme employ…
Stream Attention for far-field multi-microphone ASR
Xiaofei Wang, Yonghong Yan, Hynek Hermansky
A stream attention framework has been applied to the posterior probabilities of the deep neural network (DNN) to improve the far-field automatic speech recognition (ASR) performanc…
Relative Transfer Function Inverse Regression from Low Dimensional Manifold
Ziteng Wang, Emmanuel Vincent, Yonghong Yan
In room acoustic environments, the Relative Transfer Functions (RTFs) are controlled by few underlying modes of variability. Accordingly, they are confined to a low-dimensional man…