Sibo Tong

According to our database1, Sibo Tong authored at least 16 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Slot-Triggered Contextual Biasing For Personalized Speech Recognition Using Neural Transducers.
Proceedings of the IEEE International Conference on Acoustics, 2023

Hierarchical Attention-Based Contextual Biasing For Personalized Speech Recognition Using Neural Transducers.
Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2023

2021
A Bayesian Approach to Recurrence in Neural Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

2020
Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models.
CoRR, 2020

Lattice-Free Maximum Mutual Information Training of Multilingual Speech Recognition Systems.
Proceedings of the Interspeech 2020, 2020

2019
Unbiased Semi-Supervised LF-MMI Training Using Dropout.
Proceedings of the Interspeech 2019, 2019

Analyzing Uncertainties in Speech Recognition Using Dropout.
Proceedings of the IEEE International Conference on Acoustics, 2019

An Investigation of Multilingual ASR Using End-to-end LF-MMI.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Cross-lingual adaptation of a CTC-based multilingual acoustic model.
Speech Commun., 2018

Fast Language Adaptation Using Phonological Information.
Proceedings of the Interspeech 2018, 2018

Nasal Speech Sounds Detection Using Connectionist Temporal Classification.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model.
CoRR, 2017

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation.
Proceedings of the Interspeech 2017, 2017


2016
Multi-task joint-learning for robust voice activity detection.
Proceedings of the 10th International Symposium on Chinese Spoken Language Processing, 2016

A comparative study of robustness of deep learning approaches for VAD.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016


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