The TNT Team System Descriptions of Cantonese and Mongolian for IARPA OpenASR20

Zhao, Jing; Lv, Zhiqiang; Han, Ambyera; Wang, Guan-Bo; Shi, Gui-Xin; Kang, Jian; Yan, Jinghao; Hu, Pengfei; Huang, Shen; Zhang, Weiqiang

doi:10.21437/interspeech.2021-1063

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Dnn-based Approach To Build Acoustic Model1

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Cited by 5 publications

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“…In the end, the TNT team obtained a WER of 0.483 on the Cantonese development set and a WER of 0.402 on the evaluation set. Likewise, a WER of 0.524 was obtained on the Mongolian dev set and 0.449 on the evaluation set [78]. The Tallinn University of Technology (TalTech) team participated in OpenASR20's restricted training conditions in all 10 languages.…”

Section: Dnn-based Approach To Build Acoustic Modelmentioning

confidence: 99%

Frontier Research on Low-Resource Speech Recognition Technology

Slam,

Li,

Urouvas

2023

Sensors

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With the development of continuous speech recognition technology, users have put forward higher requirements in terms of speech recognition accuracy. Low-resource speech recognition, as a typical speech recognition technology under restricted conditions, has become a research hotspot nowadays because of its low recognition rate and great application value. Under the premise of low-resource speech recognition technology, this paper reviews the research status of feature extraction and acoustic models, and conducts research on resource expansion. Especially in terms of the technical challenges faced by this technology, solutions are proposed, and future research directions are prospected.

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Section: Dnn-based Approach To Build Acoustic Modelmentioning

confidence: 99%