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：
一、系统架构设计
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:nfor j=2:mcost = norm(query(i,:) - ref(j,:));D(i,j) = cost + min([D(i-1,j), D(i,j-1), D(i-1,j-1)]);endend% 回溯路径[dist, path] = backtrack(D);
endfunction [min_dist, path] = backtrack(D)[n,m] = size(D);path = [];min_dist = D(n,m);i = n; j = m;while i > 1 || j > 1path = [i,j; path];[~, idx] = min([D(i-1,j), D(i,j-1), D(i-1,j-1)]);switch idxcase 1: i=i-1;case 2: j=j-1;case 3: i=i-1; j=j-1;endendpath = [1,1; flipud(path)];
end
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 = 1:10record_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 = 1:10ref_feat = template_feats{word};[dist, ~] = dtw(test_mfcc, ref_feat);if dist < min_distmin_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);
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
-
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 -
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） -
抗噪处理：
% 添加维纳滤波 denoised = wdenoise(test_signal, 4);
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。
