86 citations · 363 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR
Hirofumi Inaguma, Yashesh Gaur, Liang Lu +2
Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. Howe…
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…
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition
Hu Hu, Rui Zhao, Jinyu Li +2
Recently, the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of bei…
Low Latency End-to-End Streaming Speech Recognition with a Scout Network
Chengyi Wang, Yu Wu, Shujie Liu +4
The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode. However, in the streaming mode, the Transformer model usually…
Continuous speech separation: dataset and analysis
Zhuo Chen, Takuya Yoshioka, Liang Lu +6
This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior studies on speech separation use pre-segmented signals of artificial…