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- W4322718894 abstract "In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.针对构音障碍语音识别率难以提升的问题，本文提出一种多尺度梅尔域特征图谱提取算法。该算法采用经验模态分解方法分解语音信号，分别对三个有效分量提取Fbank特征及其一阶差分，从而构成能够捕捉频域细节信息的新特征图谱。其次，由于单路神经网络在训练过程中，存在有效特征丢失及计算复杂度高的问题，本文提出一种语音识别网络模型。最后，在公开UA-Speech数据集上进行训练和解码。实验结果表明，本文方法的语音识别模型准确率达到了92.77%，因此，本文所提算法能有效提高构音障碍语音识别率。." @default.
- W4322718894 created "2023-03-03" @default.
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- W4322718894 date "2023-02-25" @default.
- W4322718894 modified "2023-09-27" @default.
- W4322718894 title "[A multiscale feature extraction algorithm for dysarthric speech recognition]." @default.
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- W4322718894 doi "https://doi.org/10.7507/1001-5515.202205049" @default.
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