Aiming at the inaccurate phoneme segmentation problem caused by the lack of considera-tion of Lao language tone changes and audio diversity in existing methods,this paper proposes an unsu-pervised phoneme segmentation method for Lao language with multi-feature interaction fusion.Firstly,self-supervised features,spectral features and pitch features are independently coded to avoid the insuffi-ciency of a single feature.Secondly,multiple independent features are gradually fused based on the at-tention mechanism,so that the model can more comprehensively capture the information of Lao lan-guage tone changes and phoneme boundaries.Finally,a learnable framework is adopted to optimize the phoneme segmentation model.The experimental results show that the proposed method improves the R-value by 27.88%on the Lao phoneme segmentation task compared with the baseline methods.
维普
万方数据