佛山科学技术学院学报（自然科学版）2024，Vol.42Issue(3)：27-34,8.
一种基于Transformer模型的特征增强算法及其应用研究
A study on a feature enhancement algorithm based on Transformer model and its application
摘要
Abstract
The Transformer model demonstrates excellent performance in the task of automatic speech recognition(ASR),but there is still room for improvement in feature extraction.This study identifies two main issues with the model:first,it focuses on extracting global feature interactions,overlooking other useful features such as local feature interactions;second,it does not fully utilize low-level feature interactions.To address these issues and enhance the model's performance in ASR tasks,we propose a Convolutional Linear Mapping(CMLP)module to enhance local feature interactions and a Low-level Feature Fusion(LF)module to integrate high-level and low-level features.By integrating these modules,we construct the CLformer model.Experimental results on two Chinese Mandarin datasets(Aishell-1 and HKUST)demonstrate that CLformer significantly improves model performance:by 0.3%on Aishell-1 and 0.5%on HKUST compared to the baseline.This validates the effectiveness of our optimization strategy.关键词
Transformer模型/自动语音识别/特征增强/局部特征/特征融合Key words
Transformer model/automatic speech recognition/feature fusion/local feature/global feature分类
信息技术与安全科学引用本文复制引用
李俊华,段志奎,于昕梅..一种基于Transformer模型的特征增强算法及其应用研究[J].佛山科学技术学院学报（自然科学版）,2024,42(3):27-34,8.基金项目
广东省普通高校重点实验室资助项目(2021KSYS008) （2021KSYS008）
