用SVM进行语音识别的MATLAB程序

以下是一个简单的基于SVM的语音识别MATLAB程序：

1. 加载语音数据和标签

load('speech_data.mat'); % 语音数据和标签

2. 分割语音信号为帧

frame_length = 256; % 每帧的长度
frame_shift = 128; % 帧移
num_frames = floor((length(speech)-frame_length)/frame_shift)+1; % 总帧数
frames = zeros(frame_length,num_frames); % 存储所有帧
for i=1:num_frames
    frames(:,i) = speech((i-1)*frame_shift+1:(i-1)*frame_shift+frame_length);
end

3. 提取MFCC特征

num_coeffs = 12; % 系数个数
num_filters = 26; % 滤波器个数
mfccs = zeros(num_coeffs,num_frames); % 存储所有MFCC特征
for i=1:num_frames
    frame = frames(:,i);
    % 预加重
    frame = filter([1 -0.97],1,frame);
    % 计算功率谱密度
    psd = abs(fft(frame)).^2/length(frame);
    % 三角滤波器组
    fmin = 0;
    fmax = 8000;
    mel_min = 1125*log(1+fmin/700);
    mel_max = 1125*log(1+fmax/700);
    mel_points = linspace(mel_min,mel_max,num_filters+2);
    hz_points = 700*(exp(mel_points/1125)-1);
    fbank = zeros(num_filters,frame_length/2+1);
    for j=1:num_filters
        low = hz_points(j);
        center = hz_points(j+1);
        high = hz_points(j+2);
        filter_points = linspace(low,high,frame_length/2+1);
        for k=1:length(filter_points)
            if filter_points(k) < center
                fbank(j,k) = (filter_points(k)-low)/(center-low);
            else
                fbank(j,k) = (high-filter_points(k))/(high-center);
            end
        end
    end
    % 应用滤波器组
    filtered = log(fbank*psd(1:frame_length/2+1));
    % 应用离散余弦变换
    dct_coeffs = dct(filtered);
    mfccs(:,i) = dct_coeffs(1:num_coeffs);
end

4. 训练SVM分类器

labels = labels+1; % 将标签从0开始编号改为1开始编号
svm_model = fitcsvm(mfccs',labels); % 训练SVM分类器

5. 进行语音识别

test_speech = % 待识别的语音信号
test_frames = zeros(frame_length,num_frames); % 存储所有帧
for i=1:num_frames
    test_frames(:,i) = test_speech((i-1)*frame_shift+1:(i-1)*frame_shift+frame_length);
end
test_mfccs = zeros(num_coeffs,num_frames); % 存储所有MFCC特征
for i=1:num_frames
    frame = test_frames(:,i);
    % 预加重
    frame = filter([1 -0.97],1,frame);
    % 计算功率谱密度
    psd = abs(fft(frame)).^2/length(frame);
    % 应用滤波器组
    filtered = log(fbank*psd(1:frame_length/2+1));
    % 应用离散余弦变换
    dct_coeffs = dct(filtered);
    test_mfccs(:,i) = dct_coeffs(1:num_coeffs);
end
predicted_labels = predict(svm_model,test_mfccs'); % 预测标签
predicted_label = mode(predicted_labels); % 取众数作为最终预测结果

以上是一个简单的基于SVM的语音识别MATLAB程序，仅供参考。实际应用中，需要更复杂的特征提取方法和更精细的模型调整。