基于dtw的孤立字识别系统matlab代码

以下是基于DTW的孤立字识别系统的MATLAB代码示例：

clear all; close all; clc;

% 载入训练集和测试集
load('isolated_train.mat');
load('isolated_test.mat');

% 提取特征
train_feature = cell(1, length(train_data));
for i = 1:length(train_data)
    train_feature{i} = mfcc(train_data{i}, train_fs);
end

test_feature = cell(1, length(test_data));
for i = 1:length(test_data)
    test_feature{i} = mfcc(test_data{i}, test_fs);
end

% 训练模型
model = struct();
for i = 1:length(train_label)
    label = train_label(i);
    if ~isfield(model, num2str(label))
        model.(num2str(label)) = {};
    end
    model.(num2str(label)) = [model.(num2str(label)); train_feature{i}];
end

% 测试模型
correct = 0;
for i = 1:length(test_label)
    test = test_feature{i};
    true_label = test_label(i);
    min_distance = Inf;
    predict_label = -1;
    for j = 1:length(fieldnames(model))
        label = str2double(fieldnames(model){j});
        template = model.(num2str(label));
        for k = 1:size(template, 1)
            distance = dtw(test', template(k, :)');
            if distance < min_distance
                min_distance = distance;
                predict_label = label;
            end
        end
    end
    if predict_label == true_label
        correct = correct + 1;
    end
end

% 输出准确率
accuracy = correct / length(test_label);
fprintf('准确率：%.2f%%\n', accuracy * 100);

这里使用了MFCC（Mel-Frequency Cepstral Coefficients）作为特征，DTW（Dynamic Time Warping）作为距离度量。其中，`isolated_train.mat`和`isolated_test.mat`是训练集和测试集的数据文件，每个文件包含一个名为`train_data`或`test_data`的单元格数组，每个单元格存储一个音频信号，以及对应的采样率`train_fs`或`test_fs`和标签`train_label`或`test_label`。特征提取过程使用了第三方库MFCC包，需要先安装并添加到MATLAB路径中。