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20172026
most citedSpeaker Adaptation for Attention-Based End-to-End Speech Recognition

41 citations · 149 across the 46 of their papers we have counts for

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Showing 2020Show all

12 papers · 1 filter

eess.AS2020

Exploring End-to-End Multi-channel ASR with Bias Information for Meeting Transcription

Xiaofei Wang, Naoyuki Kanda, Yashesh Gaur +3

Joint optimization of multi-channel front-end and automatic speech recognition (ASR) has attracted much interest. While promising results have been reported for various tasks, past…

eess.AS2020★ 2 cited

Listen, Look and Deliberate: Visual context-aware speech recognition using pre-trained text-video representations

Shahram Ghorbani, Yashesh Gaur, Yu Shi +1

In this study, we try to address the problem of leveraging visual signals to improve Automatic Speech Recognition (ASR), also known as visual context-aware ASR (VC-ASR). We explore…

eess.AS2020★ 2 cited

Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR

Naoyuki Kanda, Zhong Meng, Liang Lu +4

Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identif…

eess.AS2020

Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition

Zhong Meng, Sarangarajan Parthasarathy, Eric Sun +7

The external language models (LM) integration remains a challenging task for end-to-end (E2E) automatic speech recognition (ASR) which has no clear division between acoustic and la…

eess.AS2020★ 3 cited

Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings

Naoyuki Kanda, Xuankai Chang, Yashesh Gaur +4

Recently, an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker ident…

cs.LG2020★ 8 cited

Federated Transfer Learning with Dynamic Gradient Aggregation

Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2

In this paper, a Federated Learning (FL) simulation platform is introduced. The target scenario is Acoustic Model training based on this platform. To our knowledge, this is the fir…