Data, algorithms, and arithmetic power are the three foundational conditions
for deep learning to be effective in the application domain. Data is the focus
for developing deep learning algorithms. In practical engineering applications,
some data are affected by the conditions under which more data cannot be
obtained or the cost of obtaining data is too high, resulting in smaller data
sets (generally several hundred to several thousand) and data sizes that are
far smaller than the size of large data sets (tens of thousands). The above two
methods are based on the original dataset to generate, in the case of
insufficient data volume of the original data may not reflect all the real
environment, such as the real environment of the light, silhouette and other
information, if the amount of data is not enough, it is difficult to use a
simple transformation or neural network generative model to generate the
required data. The research in this paper firstly analyses the key points of
the data enhancement technology of graph neural network, and at the same time
introduces the composition foundation of graph neural network in depth, on the
basis of which the data enhancement technology of graph neural network is
optimized and analysed.

数据增强技术在图神经网络的深度学习算法中起到关键作用，本文对图神经网络的数据增强技术进行了分析和优化，提出了一种基于图神经网络的数据增强技术。

图神经网络数据增强技术的研究与实现

Research and Implementation of Data Enhancement Techniques for Graph  Neural Networks

In recent years, End-to-End speech recognition technology based on deep
learning has developed rapidly. Due to the lack of Turkish speech data, the
performance of Turkish speech recognition system is poor. Firstly, this paper
studies a series of speech recognition tuning technologies. The results show
that the performance of the model is the best when the data enhancement
technology combining speed perturbation with noise addition is adopted and the
beam search width is set to 16. Secondly, to maximize the use of effective
feature information and improve the accuracy of feature extraction, this paper
proposes a new feature extractor LSPC. LSPC and LiGRU network are combined to
form a shared encoder structure, and model compression is realized. The results
show that the performance of LSPC is better than MSPC and VGGnet when only
using Fbank features, and the WER is improved by 1.01% and 2.53% respectively.
Finally, based on the above two points, a new multi-feature fusion network is
proposed as the main structure of the encoder. The results show that the WER of
the proposed feature fusion network based on LSPC is improved by 0.82% and
1.94% again compared with the single feature (Fbank feature and Spectrogram
feature) extraction using LSPC. Our model achieves performance comparable to
that of advanced End-to-End models.

本文针对土耳其缺乏语音数据造成的语音识别系统性能差问题，通过采用一系列调优技术，提出了结合速度扰动和噪声叠加的数据增强技术和新型特征提取器 LSPC，并将其与 LiGRU 网络结合，形成共享编码器结构并实现模型压缩，最终提出了基于 LSPC 的多特征融合网络作为编码器的主要结构，使得该模型性能可与先进的端到端模型相媲美，该模型相比单特征提取使用 LSPC 时可将单词错误率提高 0.82%-1.94%。