116 citations · 911 across the 43 of their papers we have counts for
14 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…
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…
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…
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability
Jinyu Li, Rui Zhao, Zhong Meng +8
Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic spee…
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…
L-Vector: Neural Label Embedding for Domain Adaptation
Zhong Meng, Hu Hu, Jinyu Li +4
We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target dom…
Active Voice Authentication
Zhong Meng, M Umair Bin Altaf, Biing-Hwang +1
Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an au…
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model
Jinyu Li, Rui Zhao, Eric Sun +4
While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion…
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…
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition
Zhong Meng, Jinyu Li, Yashesh Gaur +1
Teacher-student (T/S) has shown to be effective for domain adaptation of deep neural network acoustic models in hybrid speech recognition systems. In this work, we extend the T/S l…
Character-Aware Attention-Based End-to-End Speech Recognition
Zhong Meng, Yashesh Gaur, Jinyu Li +1
Predicting words and subword units (WSUs) as the output has shown to be effective for the attention-based encoder-decoder (AED) model in end-to-end speech recognition. However, as…
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…