b'Liang Lu'

research

∙ 01/24/2022

Endpoint Detection for Streaming End-to-End Multi-talker ASR

Streaming end-to-end multi-talker speech recognition aims at transcribin...

0 Liang Lu, et al. ∙

research

∙ 09/17/2021

Continuous Streaming Multi-Talker ASR with Dual-path Transducers

Streaming recognition of multi-talker conversations has so far been eval...

0 Desh Raj, et al. ∙

research

∙ 06/04/2021

Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition

Integrating external language models (LMs) into end-to-end (E2E) models ...

0 Zhong Meng, et al. ∙

research

∙ 04/05/2021

Streaming Multi-talker Speech Recognition with Joint Speaker Identification

In multi-talker scenarios such as meetings and conversations, speech pro...

0 Liang Lu, et al. ∙

research

∙ 02/02/2021

Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition

The efficacy of external language model (LM) integration with existing e...

0 Zhong Meng, et al. ∙

research

∙ 11/26/2020

Streaming end-to-end multi-talker speech recognition

End-to-end multi-talker speech recognition is an emerging research trend...

0 Liang Lu, et al. ∙

research

∙ 11/03/2020

Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR

Recently, an end-to-end speaker-attributed automatic speech recognition ...

0 Naoyuki Kanda, et al. ∙

research

∙ 11/03/2020

Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition

The external language models (LM) integration remains a challenging task...

0 Zhong Meng, et al. ∙

research

∙ 10/23/2020

On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer

Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end...

0 Liang Lu, et al. ∙

research

∙ 05/19/2020

Exploring Transformers for Large-Scale Speech Recognition

While recurrent neural networks still largely define state-of-the-art sp...

0 Liang Lu, et al. ∙

research

∙ 05/01/2020

Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition

Recently, the recurrent neural network transducer (RNN-T) architecture h...

0 Hu Hu, et al. ∙

research

∙ 04/10/2020

Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR

Recently, a few novel streaming attention-based sequence-to-sequence (S2...

0 Hirofumi Inaguma, et al. ∙

research

∙ 01/30/2020

Continuous speech separation: dataset and analysis

This paper describes a dataset and protocols for evaluating continuous s...

0 Zhuo Chen, et al. ∙

research

∙ 12/06/2019

Semantic Mask for Transformer based End-to-End Speech Recognition

Attention-based encoder-decoder model has achieved impressive results fo...

0 Chengyi Wang, et al. ∙

research

∙ 10/23/2019

A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic Models

Transformer with self-attention has achieved great success in the area o...

0 Liang Lu, et al. ∙

research

∙ 09/09/2019

Self-Teaching Networks

We propose self-teaching networks to improve the generalization capacity...

0 Liang Lu, et al. ∙

research

∙ 07/12/2019

Pykaldi2: Yet another speech toolkit based on Kaldi and Pytorch

We introduce PyKaldi2 speech recognition toolkit implemented based on Ka...

0 Liang Lu, et al. ∙

research

∙ 10/28/2017

A Study of All-Convolutional Encoders for Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a popular sequence predic...

0 Kalpesh Krishna, et al. ∙

research

∙ 08/01/2017

End-to-End Neural Segmental Models for Speech Recognition

Segmental models are an alternative to frame-based models for sequence p...

0 Hao Tang, et al. ∙

research

∙ 06/28/2017

Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations

Neural network acoustic models have significantly advanced state of the ...

0 Liang Lu, et al. ∙

research

∙ 04/05/2017

Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

End-to-end training of deep learning-based models allows for implicit le...

0 Shubham Toshniwal, et al. ∙

research

∙ 02/21/2017

Multitask Learning with CTC and Segmental CRF for Speech Recognition

Segmental conditional random fields (SCRFs) and connectionist temporal c...

0 Liang Lu, et al. ∙

research

∙ 10/18/2016

Small-footprint Highway Deep Neural Networks for Speech Recognition

State-of-the-art speech recognition systems typically employ neural netw...

0 Liang Lu, et al. ∙

research

∙ 09/26/2016

Multiplicative LSTM for sequence modelling

We introduce multiplicative LSTM (mLSTM), a recurrent neural network arc...

0 Ben Krause, et al. ∙

research

∙ 08/02/2016

Knowledge Distillation for Small-footprint Highway Networks

Deep learning has significantly advanced state-of-the-art of speech reco...

0 Liang Lu, et al. ∙

research

∙ 07/07/2016

Sequence Training and Adaptation of Highway Deep Neural Networks

Highway deep neural network (HDNN) is a type of depth-gated feedforward ...

0 Liang Lu, et al. ∙

research

∙ 03/01/2016

Segmental Recurrent Neural Networks for End-to-end Speech Recognition

We study the segmental recurrent neural network for end-to-end acoustic ...

0 Liang Lu, et al. ∙

research

∙ 12/14/2015

Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition

For speech recognition, deep neural networks (DNNs) have significantly i...

0 Liang Lu, et al. ∙

research

∙ 10/31/2015

Top-down Tree Long Short-Term Memory Networks

Long Short-Term Memory (LSTM) networks, a type of recurrent neural netwo...

0 Xingxing Zhang, et al. ∙

research

∙ 11/04/2014

Tied Probabilistic Linear Discriminant Analysis for Speech Recognition

Acoustic models using probabilistic linear discriminant analysis (PLDA) ...

0 Liang Lu, et al. ∙

Liang Lu

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