山东理工大学学报（自然科学版）2026，Vol.40Issue(1)：9-15,20,8.
基于一维卷积网络和自注意力机制的语种识别算法
Language recognition algorithm based on one-dimensional convolution network and self-attention mechanism
摘要
Abstract
Language recognition algorithms based on embedded vector representation can achieve excellent results in ideal environment.However,in real-world scenarios,such as dialect recognition in case analysis,performance is often compromised by noise.Current algorithms are not significantly robust for language recognition modeling in environment with low signal-to-noise ratio.In this paper,a language embedded vector representation algorithm based on self-attention mechanism and one-dimensional convo-lutional neural network is proposed.The algorithm uses one-dimensional convolutional neural network in-stead of the traditional time-delay neural network to fuse the time sequence information more efficiently at the audio frame level.Meanwhile,self-attention mechanism is introduced to enhance the model's ability to focus on specific language features,even in noisy environment,thereby enhancing the robustness of the model for language recognition in noisy environment.By testing the language audio data in a simulated noisy environment,the experimental results show that compared with the mainstream algorithms,the pro-posed algorithm achieves a 2.21%improvement in recognition accuracy for 10-second audio in noisy en-vironments.It also reduces the equal error rate by 0.24%,and the average detection cost by 0.20%.关键词
语种识别/自注意力机制/嵌入式向量/卷积神经网络Key words
language identification/self-attention mechanism/embedded vector/convolutional neural network分类
自科综合引用本文复制引用
宋朝阳,郭永帅..基于一维卷积网络和自注意力机制的语种识别算法[J].山东理工大学学报（自然科学版）,2026,40(1):9-15,20,8.基金项目
安徽省高校自然科学重点研究项目(KJ2020A1125,2023AH053016) （KJ2020A1125,2023AH053016）
安徽省高等学校省级质量工程项目(2021jyxm0222) （2021jyxm0222）
