Quoc Bao Nguyen


2020

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Development of Smartcall Vietnamese Text-to-Speech for VLSP 2020
Khuong Duy Trieu | Ba Quyen Dam | Quoc Bao Nguyen
Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing

2015

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The IOIT English ASR system for IWSLT 2015
Van Huy Nguyen | Quoc Bao Nguyen | Tat Thang Vu | Chi Mai Luong
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

2014

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The speech recognition systems of IOIT for IWSLT 2014
Quoc Bao Nguyen | Tat Thang Vu | Chi Mai Luong
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the speech recognition systems of IOIT for IWSLT 2014 TED ASR track. This year, we focus on improving acoustic model for the systems using two main approaches of deep neural network which are hybrid and bottleneck feature systems. These two subsystems are combined using lattice Minimum Bayes-Risk decoding. On the 2013 evaluations set, which serves as a progress test set, we were able to reduce the word error rate of our transcription systems from 27.2% to 24.0%, a relative reduction of 11.7%.

2013

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The 2013 KIT IWSLT speech-to-text systems for German and English
Kevin Kilgour | Christian Mohr | Michael Heck | Quoc Bao Nguyen | Van Huy Nguyen | Evgeniy Shin | Igor Tseyzer | Jonas Gehring | Markus Müller | Matthias Sperber | Sebastian Stüker | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2013 IWSLT TED ASR track. The systems consist of multiple subsystems that are combinations of different front-ends, e.g. MVDR-MFCC based and lMel based ones, GMM and NN acoustic models and different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR.