1.9k citations · 2k across the 63 of their papers we have counts for
10 papers · 1 filter
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…
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…
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Desh Raj, Pavel Denisov, Zhuo Chen +11
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in syst…
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…
Streaming end-to-end multi-talker speech recognition
Liang Lu, Naoyuki Kanda, Jinyu Li +1
End-to-end multi-talker speech recognition is an emerging research trend in the speech community due to its vast potential in applications such as conversation and meeting transcri…
Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
Xiong Xiao, Naoyuki Kanda, Zhuo Chen +10
This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recogniti…
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer
Liang Lu, Zhong Meng, Naoyuki Kanda +2
Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the…
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…
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang +4
We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped…
Serialized Output Training for End-to-End Overlapped Speech Recognition
Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang +2
This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instea…