86 citations · 363 across the 30 of their papers we have counts for
32 papers
Endpoint Detection for Streaming End-to-End Multi-talker ASR
Liang Lu, Jinyu Li, Yifan Gong
Streaming end-to-end multi-talker speech recognition aims at transcribing the overlapped speech from conversations or meetings with an all-neural model in a streaming fashion, whic…
Continuous Streaming Multi-Talker ASR with Dual-path Transducers
Desh Raj, Liang Lu, Zhuo Chen +2
Streaming recognition of multi-talker conversations has so far been evaluated only for 2-speaker single-turn sessions. In this paper, we investigate it for multi-turn meetings cont…
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition
Zhong Meng, Yu Wu, Naoyuki Kanda +6
Integrating external language models (LMs) into end-to-end (E2E) models remains a challenging task for domain-adaptive speech recognition. Recently, internal language model estimat…
Streaming Multi-talker Speech Recognition with Joint Speaker Identification
Liang Lu, Naoyuki Kanda, Jinyu Li +1
In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to transcribe the audio as well as identify the speakers for downstream…
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition
Zhong Meng, Naoyuki Kanda, Yashesh Gaur +6
The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal…
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…
Semantic Mask for Transformer based End-to-End Speech Recognition
Chengyi Wang, Yu Wu, Yujiao Du +7
Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of t…
A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic Models
Liang Lu
Transformer with self-attention has achieved great success in the area of nature language processing. Recently, there have been a few studies on transformer for end-to-end speech r…
Self-Teaching Networks
Liang Lu, Eric Sun, Yifan Gong
We propose self-teaching networks to improve the generalization capacity of deep neural networks. The idea is to generate soft supervision labels using the output layer for trainin…
PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch
Liang Lu, Xiong Xiao, Zhuo Chen +1
We introduce PyKaldi2 speech recognition toolkit implemented based on Kaldi and PyTorch. While similar toolkits are available built on top of the two, a key feature of PyKaldi2 is…
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Kalpesh Krishna, Liang Lu, Kevin Gimpel +1
Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural…
End-to-End Neural Segmental Models for Speech Recognition
Hao Tang, Liang Lu, Lingpeng Kong +5
Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at…
Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations
Liang Lu
Neural network acoustic models have significantly advanced state of the art speech recognition over the past few years. However, they are usually computationally expensive due to t…
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
Shubham Toshniwal, Hao Tang, Liang Lu +1
End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignore…
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Liang Lu, Lingpeng Kong, Chris Dyer +1
Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition mod…
Adaptive DCTNet for Audio Signal Classification
Yin Xian, Yunchen Pu, Zhe Gan +2
In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adapti…