基于MFCC特征提取和DTW算法的孤立字词识别

基于MFCC特征提取和DTW算法的孤立字词识别的MATLAB实现代码

1. MFCC特征提取

function mfccs = extractMFCC(signal, fs)
    % 提取MFCC特征
    % 输入：
    %   signal - 语音信号
    %   fs - 采样频率
    % 输出：
    %   mfccs - 提取的MFCC特征

    % 预加重
    preemph = [1, -0.97];
    signal = filter(preemph, 1, signal);

    % 分帧
    frame_len = 25; % 每帧长度为25ms
    frame_shift = 10; % 帧移为10ms
    frame_size = round(frame_len * fs / 1000);
    frame_shift_size = round(frame_shift * fs / 1000);
    frames = buffer(signal, frame_size, frame_size - frame_shift_size, 'nodelay');

    % 加窗
    window = hamming(frame_size);
    frames = bsxfun(@times, frames, window);

    % FFT
    NFFT = 2^nextpow2(frame_size);
    mag_frames = abs(fft(frames, NFFT, 2));

    % Mel滤波器组
    low_freq = 0;
    high_freq = fs / 2;
    nfilt = 26;
    mel_pts = linspace(hz2mel(low_freq), hz2mel(high_freq), nfilt + 2);
    hz_pts = mel2hz(mel_pts);
    bin = floor((NFFT + 1) * hz_pts / fs);

    filter_bank = zeros(nfilt, floor(NFFT / 2 + 1));
    for j = 1:nfilt
        for i = bin(j):bin(j + 1)
            filter_bank(j, i) = (i - bin(j)) / (bin(j + 1) - bin(j));
        end
        for i = bin(j + 1):bin(j + 2)
            filter_bank(j, i) = (bin(j + 2) - i) / (bin(j + 2) - bin(j + 1));
        end
    end

    % 应用Mel滤波器组
    filtered_frames = filter_bank * mag_frames(:, 1:NFFT / 2 + 1)';

    % 取对数
    log_filtered_frames = log(filtered_frames + eps);

    % DCT
    num_ceps = 12;
    mfccs = dct(log_filtered_frames);
    mfccs = mfccs(1:num_ceps, :);
end

function mel = hz2mel(hz)
    % Hz转Mel
    mel = 2595 * log10(1 + hz / 700);
end

function hz = mel2hz(mel)
    % Mel转Hz
    hz = 700 * (10 .^ (mel / 2595) - 1);
end

2. DTW算法

function [distance, path] = dtw(signal1, signal2)
    % DTW算法
    % 输入：
    %   signal1 - 第一个信号
    %   signal2 - 第二个信号
    % 输出：
    %   distance - 两个信号之间的DTW距离
    %   path - 最佳匹配路径

    % 计算累积距离矩阵
    [rows1, cols1] = size(signal1);
    [rows2, cols2] = size(signal2);
    cost_matrix = zeros(rows1, rows2);
    for i = 1:rows1
        for j = 1:rows2
            cost_matrix(i, j) = norm(signal1(i, :) - signal2(j, :))^2;
        end
    end

    dtw_matrix = zeros(rows1 + 1, rows2 + 1);
    dtw_matrix(2:end, 2:end) = inf;
    dtw_matrix(1, 1) = 0;

    for i = 2:rows1 + 1
        for j = 2:rows2 + 1
            cost = cost_matrix(i - 1, j - 1);
            last_min = min([dtw_matrix(i - 1, j), dtw_matrix(i, j - 1), dtw_matrix(i - 1, j - 1)]);
            dtw_matrix(i, j) = cost + last_min;
        end
    end

    % 回溯找到最佳路径
    i = rows1 + 1;
    j = rows2 + 1;
    path = [];
    while i > 1 && j > 1
        path = [i - 1, j - 1; path];
        [~, idx] = min([dtw_matrix(i - 1, j), dtw_matrix(i, j - 1), dtw_matrix(i - 1, j - 1)]);
        if idx == 1
            i = i - 1;
        elseif idx == 2
            j = j - 1;
        else
            i = i - 1;
            j = j - 1;
        end
    end

    distance = dtw_matrix(rows1 + 1, rows2 + 1);
end

3. 孤立字词识别

function isolated_word_recognition()
    % 孤立字词识别
    % 假设已经提取好了参考模板的MFCC特征并存储在ref_mfccs.mat文件中
    load('ref_mfccs.mat'); % 加载参考模板的MFCC特征

    % 读取测试语音信号
    [test_signal, fs] = audioread('test.wav'); % 替换为你的测试语音文件路径
    test_mfccs = extractMFCC(test_signal, fs); % 提取测试语音的MFCC特征

    % 初始化最小距离和识别结果
    min_distance = inf;
    recognized_word = '';

    % 遍历参考模板进行匹配
    for i = 1:length(ref_mfccs)
        [distance, ~] = dtw(test_mfccs', ref_mfccs{i}'); % DTW匹配
        if distance < min_distance
            min_distance = distance;
            recognized_word = words{i}; % 假设words数组存储了参考模板对应的单词
        end
    end

    % 输出识别结果
    disp(['识别结果：', recognized_word]);
end

4. 主函数

function main()
    % 主函数
    isolated_word_recognition();
end

参考代码 基于mfcc和DTW的孤立字词识别源码 www.youwenfan.com/contentcnd/96990.html

说明

1. MFCC特征提取：提取语音信号的MFCC特征，包括预加重、分帧、加窗、FFT、Mel滤波器组、取对数和DCT等步骤。
2. DTW算法：实现DTW算法，计算两个信号之间的最佳匹配路径和距离。
3. 孤立字词识别：加载参考模板的MFCC特征，提取测试语音的MFCC特征，使用DTW算法进行匹配，输出识别结果。

运行主函数main()即可完成孤立字词的识别。请确保将参考模板的MFCC特征存储为ref_mfccs.mat文件，并将测试语音文件路径替换为实际路径。