Frontier Research on Low-Resource Speech Recognition Technology

被引：3
|
作者：
Slam, Wushour [1 ]
Li, Yanan [1 ]
Urouvas, Nurmamet [1 ]
机构：
[1] Xinjiang Univ, Coll Informat Sci & Engn, Xinjiang Lab Multilanguage Informat Technol, Xinjiang Multilingual Informat Technol Res Ctr, Urumqi 830046, Peoples R China
来源：
SENSORS | 2023年 / 23卷 / 22期
关键词：
low-resource speech recognition; deep feature extraction; acoustic models; resource expansion; COVARIANCE MATRICES; SPEAKER ADAPTATION; DATA AUGMENTATION; NEURAL-NETWORKS; FEATURES; SYSTEM; ASR; LANGUAGES; LEXICONS; IMPROVE;
D O I：
10.3390/s23229096
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
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.
引用
收藏
页数：47
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