从光谱图像自动语言识别

2021 IEEE International Conference on Smart Information Systems and Technologies (SIST) Pub Date : 2021-04-28 DOI:10.1109/SIST50301.2021.9465996

Aizada Kaiyr, S. Kadyrov, A. Bogdanchikov

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引用次数: 1

摘要

该工作的主要思想是将CNN和LSTM算法应用于预处理后的音频数据转换为频谱图。实验中使用英语、哈萨克语、法语、德语、意大利语、俄语和西班牙语等7种语言对算法进行测试，训练准确率超过99%，在测试集上准确率最高达到94.28%。最后讨论了哈萨克语分类的100%正确率以及数据集对分类结果可能产生的影响。

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Automatic Language Identification from Spectorgam Images

The main idea of the work is to apply CNN and LSTM algorithms on the preprocessed audio data converted to spectrograms. In the experiment 7 languages are used English, Kazakh, French, German, Italian, Russian and Spanish to test that algorithm which show over 99% training accuracy and the maximum of 94.28% accuracy on the test set. In the end there is a discussion of the 100% of classification of the Kazakh language and the possible influences of dataset on the result.

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来源期刊

2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)

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