20 citations · 92 across the 18 of their papers we have counts for
18 papers
Hybrid Attention-based Encoder-decoder Model for Efficient Language Model Adaptation
Shaoshi Ling, Guoli Ye, Rui Zhao +1
The attention-based encoder-decoder (AED) speech recognition model has been widely successful in recent years. However, the joint optimization of acoustic model and language model…
Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models
Rui Zhao, Jian Xue, Partha Parthasarathy +2
Neural transducer is now the most popular end-to-end model for speech recognition, due to its naturally streaming ability. However, it is challenging to adapt it with text-only dat…
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer
Xun Gong, Yu Wu, Jinyu Li +4
Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is mor…
On Addressing Practical Challenges for RNN-Transducer
Rui Zhao, Jian Xue, Jinyu Li +3
In this paper, several works are proposed to address practical challenges for deploying RNN Transducer (RNN-T) based speech recognition system. These challenges are adapting a well…
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…
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System
Vikas Joshi, Rui Zhao, Rupesh R. Mehta +2
Transfer learning (TL) is widely used in conventional hybrid automatic speech recognition (ASR) system, to transfer the knowledge from source to target language. TL can be applied…
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…
On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Jinyu Li, Yu Wu, Yashesh Gaur +3
Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E method…
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…
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…
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…
Acoustic-To-Word Model Without OOV
Jinyu Li, Guoli Ye, Rui Zhao +2
Recently, the acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion was shown as a natural end-to-end model directly targeting words as output u…
Improved training for online end-to-end speech recognition systems
Suyoun Kim, Michael L. Seltzer, Jinyu Li +1
Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training. Otherwise, the networks may fail to find a good local optimu…
Large-Scale Domain Adaptation via Teacher-Student Learning
Jinyu Li, Michael L. Seltzer, Xi Wang +2
High accuracy speech recognition requires a large amount of transcribed data for supervised training. In the absence of such data, domain adaptation of a well-trained acoustic mode…