Efficiency of End-to-End Speech Recognition for Languages with Scarce Resources

被引：0
|
作者：
Rudzionis, Vytautas [1 ]
Malukas, Ugnius [2 ]
Lopata, Audrius [2 ]
机构：
[1] Vilnius Univ, Kaunas Fac, Muitines 8, Kaunas, Lithuania
[2] Kaunas Univ Technol, Studentu 50, Kaunas, Lithuania
来源：
INFORMATION AND SOFTWARE TECHNOLOGIES, ICIST 2022 | 2022年 / 1665卷
关键词：
Speech recognition; Hybrid methods; Machine learning; End-to-end speech recognition;
D O I：
10.1007/978-3-031-16302-9_20
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Modern deep learning based speech recognition methods allow for achieving phenomenal speech recognition accuracy. But it requires enormous amounts of data to train such systems to achieve high recognition accuracy. Many less widely spoken languages simply do not possess the necessary amounts of speech corpora. The paper presents attempts to evaluate DeepSpeech-based speech recognition efficiency with the limited amounts of training data available and the ways to improve the accuracy. The experiments showed that the accuracy of DeepSpeech2 recognizer with about 100 h of speech corpora used for training is quite modest but the application of simple grammatical constraints allowed to reduce the word error rate to 23-25%.
引用
收藏
页码：259 / 264
页数：6
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