20 citations · 57 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Improving RNN Transducer Modeling for End-to-End Speech Recognition
Jinyu Li, Rui Zhao, Hu Hu +1
In the last few years, an emerging trend in automatic speech recognition research is the study of end-to-end (E2E) systems. Connectionist Temporal Classification (CTC), Attention E…
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units
Amit Das, Jinyu Li, Guoli Ye +2
The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issue…
Developing Far-Field Speaker System Via Teacher-Student Learning
Jinyu Li, Rui Zhao, Zhuo Chen +4
In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt…
Advancing Acoustic-to-Word CTC Model
Jinyu Li, Guoli Ye, Amit Das +2
The acoustic-to-word model based on the connectionist temporal classification (CTC) criterion was shown as a natural end-to-end (E2E) model directly targeting words as output units…
Advancing Connectionist Temporal Classification With Attention Modeling
Amit Das, Jinyu Li, Rui Zhao +1
In this study, we propose advancing all-neural speech recognition by directly incorporating attention modeling within the Connectionist Temporal Classification (CTC) framework. In…