School of Zhejiang International Studies University, Hangzhou 310023, China.
Comput Intell Neurosci. 2022 Jul 8;2022:4286659. doi: 10.1155/2022/4286659. eCollection 2022.
Due to the large French vocabulary, how quickly retrieve and accurately identify the required vocabulary is still a big challenge in French learning. In view of the above problems, we introduce a deep learning algorithm in this study to upgrade and optimize the retrieval system of French words and optimize the acquisition speed of speech words data and the recognition accuracy of speech words, so as to meet the needs of users for word retrieval. The results show that the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 11.65% to 4.25% in the WER criterion, with a maximum reduction of 7.4%; the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 13.52% to 4.4% in the SER criterion. The training methods of fast retrieval and extraction of the SGD synchronous update network and alternate update network parameters in French speech vocabulary reduced from the highest 582 ms to 351 ms in the response time criterion, with a maximum reduction of 8.84%; the maximum reduction of 39.7%. In French speech vocabulary, SGD synchronous updating network and alternating updating network parameter algorithm are used to quickly retrieve and extract French words. When the number of iterations reaches 120, the model fitting accuracy of the training set reaches 90.05%, while the model can reach 94.5% in the test set. The system has a stronger generalization ability and a higher speech vocabulary recognition rate to meet the practical requirements.
由于法语词汇量较大，如何快速检索和准确识别所需词汇仍然是法语学习中的一大挑战。针对上述问题，我们在本研究中引入了一种深度学习算法，以升级和优化法语单词的检索系统，并优化语音单词数据的获取速度和语音单词的识别准确率，从而满足用户对单词检索的需求。结果表明，SGD 同步更新网络和交替更新网络参数的两种训练方法用于快速检索和提取法语语音词汇，在 WER 标准下，误码率从最高的 11.65%降低到 4.25%，最大降低 7.4%；在 SER 标准下，误码率从最高的 13.52%降低到 4.4%。在 SGD 同步更新网络和交替更新网络参数的两种训练方法中，用于快速检索和提取法语语音词汇的训练方法在响应时间标准下，从最高的 582ms 降低到 351ms，最大降低 8.84%；在法语语音词汇中，SGD 同步更新网络和交替更新网络参数算法用于快速检索和提取法语单词。当迭代次数达到 120 次时，训练集的模型拟合精度达到 90.05%，而测试集的模型可以达到 94.5%。该系统具有更强的泛化能力和更高的语音词汇识别率，能够满足实际需求。
