6 citations · 19 across the 17 of their papers we have counts for
17 papers
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers
Prashant Serai, Peidong Wang, Eric Fosler-Lussier
Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modelin…
DiariST: Streaming Speech Translation with Speaker Diarization
Mu Yang, Naoyuki Kanda, Xiaofei Wang +5
End-to-end speech translation (ST) for conversation recordings involves several under-explored challenges such as speaker diarization (SD) without accurate word time stamps and han…
Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training
Eric Sun, Jinyu Li, Yuxuan Hu +8
We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users dur…
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Zili Huang, Zhuo Chen, Naoyuki Kanda +6
Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers
Peidong Wang, Eric Sun, Jian Xue +5
Automatic speech recognition (ASR) and speech translation (ST) can both use neural transducers as the model structure. It is thus possible to use a single transducer model to perfo…
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability
Jian Xue, Peidong Wang, Jinyu Li +1
In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target l…
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?
Sanyuan Chen, Yu Wu, Chengyi Wang +8
Recently, self-supervised learning (SSL) has demonstrated strong performance in speaker recognition, even if the pre-training objective is designed for speech recognition. In this…
Large-Scale Streaming End-to-End Speech Translation with Neural Transducers
Jian Xue, Peidong Wang, Jinyu Li +2
Neural transducers have been widely used in automatic speech recognition (ASR). In this paper, we introduce it to streaming end-to-end speech translation (ST), which aims to conver…
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition
Yufeng Yang, Peidong Wang, DeLiang Wang
This study addresses robust automatic speech recognition (ASR) by introducing a Conformer-based acoustic model. The proposed model builds on the wide residual bi-directional long s…
Continuous Speech Separation with Recurrent Selective Attention Network
Yixuan Zhang, Zhuo Chen, Jian Wu +4
While permutation invariant training (PIT) based continuous speech separation (CSS) significantly improves the conversation transcription accuracy, it often suffers from speech lea…
Efficient End-to-End Speech Recognition Using Performers in Conformers
Peidong Wang, DeLiang Wang
On-device end-to-end speech recognition poses a high requirement on model efficiency. Most prior works improve the efficiency by reducing model sizes. We propose to reduce the comp…
Speaker Separation Using Speaker Inventories and Estimated Speech
Peidong Wang, Zhuo Chen, DeLiang Wang +2
We propose speaker separation using speaker inventories and estimated speech (SSUSIES), a framework leveraging speaker profiles and estimated speech for speaker separation. SSUSIES…
Multitask Training with Text Data for End-to-End Speech Recognition
Peidong Wang, Tara N. Sainath, Ron J. Weiss
We propose a multitask training method for attention-based end-to-end speech recognition models. We regularize the decoder in a listen, attend, and spell model by multitask trainin…
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
Zhong-Qiu Wang, Peidong Wang, DeLiang Wang
We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditio…
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
Peidong Wang, Ke Tan, DeLiang Wang
Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago. Although enhanced speech has been demonstrated to have better intell…
Incorporating Language Level Information into Acoustic Models
Peidong Wang, Deliang Wang
This paper proposed a class of novel Deep Recurrent Neural Networks which can incorporate language-level information into acoustic models. For simplicity, we named these networks R…
Recurrent Deep Stacking Networks for Speech Recognition
Peidong Wang, Zhongqiu Wang, Deliang Wang
This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks. In the paper, we also proposed a more efficie…