基于MATLAB的DTW孤立词识别实现

基于MATLAB的DTW孤立词识别实现，包含语音处理、特征提取、模板匹配及GUI设计等核心模块：

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一、系统架构设计

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二、核心模块实现

1. 语音预处理

%% 预加重滤波
pre_emph = [1 -0.9375]; % 一阶高通滤波器
x = filter(pre_emph, 1, raw_signal);

%% 分帧加窗
frame_len = 240; % 帧长240ms (8kHz采样率)
frame_inc = 80;  % 帧移80ms
frames = enframe(x, frame_len, frame_inc);
ham_win = hamming(frame_len);
frames = frames .* ham_win;

%% 端点检测（双门限法）
[energy, zcr] = endpoint_detection(frames);
start_idx = find(energy > energy_thr & zcr > zcr_thr);
end_idx = find(energy < energy_thr & zcr < zcr_thr);
clean_frames = frames(start_idx:end_idx-1,:);

2. MFCC特征提取

function mfcc_feat = extract_mfcc(frames, fs)
    % 参数设置
    num_ceps = 12;    % 倒谱系数数量
    num_filt = 24;    % Mel滤波器数量
    fft_len = 512;    % FFT长度
    
    % 预加重
    x = filter([1 -0.9375],1,frames);
    
    % 计算功率谱
    power_spectrum = abs(fft(x, fft_len)).^2;
    
    % Mel滤波器组
    mel_bank = melbankm(num_filt, fft_len, fs, 0, 0.5);
    
    % 滤波器组应用
    filtered = power_spectrum * mel_bank';
    
    % DCT变换
    mfcc_feat = dct(log(filtered));
    
    % 一阶差分
    delta_feat = diff(mfcc_feat, 1, 2);
    delta_delta_feat = diff(mfcc_feat, 2, 2);
    
    % 合并特征
    mfcc_feat = [mfcc_feat(:,2:end-1), delta_feat, delta_delta_feat];
end

3. DTW算法实现

function [dist, path] = dtw(query, ref)
    % 动态时间规整算法
    [n,m] = size(query,1), size(ref,1);
    D = inf(n,m);
    D(1,1) = 0;
    
    % 计算距离矩阵
    for i=2:n
        for j=2:m
            cost = norm(query(i,:) - ref(j,:));
            D(i,j) = cost + min([D(i-1,j), D(i,j-1), D(i-1,j-1)]);
        end
    end
    
    % 回溯路径
    [dist, path] = backtrack(D);
end

function [min_dist, path] = backtrack(D)
    [n,m] = size(D);
    path = [];
    min_dist = D(n,m);
    
    i = n; j = m;
    while i > 1 || j > 1
        path = [i,j; path];
        [~, idx] = min([D(i-1,j), D(i,j-1), D(i-1,j-1)]);
        switch idx
            case 1: i=i-1;
            case 2: j=j-1;
            case 3: i=i-1; j=j-1;
        end
    end
    path = [1,1; flipud(path)];
end

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三、完整系统实现流程

1. 语音数据库构建

%% 录制模板语音
fs = 8000; % 采样率
template_dir = 'templates/';
for word = 1:10
    record_file = sprintf('%s%d.wav', template_dir, word);
    record_speech(record_file, fs); % 自定义录音函数
end

2. 模板特征库生成

template_feats = struct();
for word = 1:10
    [y,fs] = audioread(sprintf('templates/%d.wav', word));
    frames = enframe(y, 240, 80);
    mfcc_feat = extract_mfcc(frames, fs);
    template_feats{word} = mean(mfcc_feat,1); % 模板平均
end

3. 实时识别模块

%% 语音输入
recorder = audiorecorder(8000,16,1);
recordblocking(recorder, 2); % 录制2秒语音
test_signal = recorder.AudioData;

%% 特征提取
test_frames = enframe(test_signal, 240, 80);
test_mfcc = extract_mfcc(test_frames, 8000);

%% DTW匹配
min_dist = inf;
best_match = 0;
for word = 1:10
    ref_feat = template_feats{word};
    [dist, ~] = dtw(test_mfcc, ref_feat);
    if dist < min_dist
        min_dist = dist;
        best_match = word;
    end
end

%% 结果显示
fprintf('识别结果：%d (距离：%.2f)\n', best_match, min_dist);

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四、GUI界面设计（使用GUIDE）

%% 创建GUI组件
fig = uifigure('Name','DTW语音识别系统');
btn_record = uibutton(fig, 'Text','开始录音',...
    'Position',[50 300 100 30], 'ButtonPushedFcn', @(btn,event) start_recording());
btn_recognize = uibutton(fig, 'Text','开始识别',...
    'Position',[200 300 100 30], 'ButtonPushedFcn', @(btn,event) do_recognition());
ax_wave = uiaxes(fig, 'Position',[0.1 0.3 0.8 0.6]);
xlabel(ax_wave,'时间(s)'); ylabel('幅值');

%% 录音回调函数
function start_recording()
    global recorder;
    recorder = audiorecorder(8000,16,1);
    recordblocking(recorder, 2);
    plot(recorder.Time, recorder.AudioData);
end

%% 识别回调函数
function do_recognition()
    global recorder;
    test_signal = recorder.AudioData;
    % 调用识别流程...
end

参考代码 用Matlab实现DTW孤立词识别 www.youwenfan.com/contentcnn/78958.html

五、性能优化

1. 特征降维：使用PCA压缩MFCC维度（保留95%方差）

   [coeff, score] = pca(template_feats{1}');
   reduced_feat = score(:,1:6); % 保留前6个主成分
   

2. 加速技巧： 使用快速DTW算法（dtw_fast函数） 限制搜索范围（设置最大时间规整因子）

3. 抗噪处理：

   % 添加维纳滤波
   denoised = wdenoise(test_signal, 4);
   

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该方法通过MFCC特征提取和DTW匹配实现了高精度的孤立词识别，实际应用中建议结合深度学习方法（如CNN+BiLSTM）进一步提升性能。