matlab语音识别项目

% main.m % 主文件：组织流程，包括数据读取、预处理、特征提取、 % GMM-HMM 模型训练、测试与结果评估 clear; clc; close all; %% 1. 参数设置 trainAudioFolder = 'data/train'; % 训练集语音文件夹 testAudioFolder = 'data/test'; % 测试集语音文件夹 fs = 8000; % 采样率（示例设置，需根据实际情况调整） nGaussian = 8; % GMM中设置的混合高斯数目，可根据实际需求调整 %% 2. 读取并预处理训练集数据 disp('>>> 开始读取并预处理训练集数据...'); [trainSignals, trainLabels] = loadAudioData(trainAudioFolder, fs); % 对训练语音进行预处理（如降噪、过滤等） for i = 1:length(trainSignals) trainSignals{i} = preprocess_audio(trainSignals{i}, fs); end %% 3. 提取MFCC特征（示例：每条语音对应一个特征矩阵） disp('>>> 正在提取训练集MFCC特征...'); trainFeaturesCell = cell(length(trainSignals), 1); for i = 1:length(trainSignals) trainFeaturesCell{i} = extract_mfcc(trainSignals{i}, fs); end %% 4. 训练GMM-HMM模型 disp('>>> 训练GMM-HMM模型...'); model = train_gmm_hmm(trainFeaturesCell, trainLabels, nGaussian); %% 5. 读取并预处理测试集数据 disp('>>> 开始读取并预处理测试集数据...'); [testSignals, testLabels] = loadAudioData(testAudioFolder, fs); for i = 1:length(testSignals) testSignals{i} = preprocess_audio(testSignals{i}, fs); end %% 6. 提取测试集MFCC特征，进行识别测试 disp('>>> 提取测试集特征并进行识别...'); testFeaturesCell = cell(length(testSignals), 1); for i = 1:length(testSignals) testFeaturesCell{i} = extract_mfcc(testSignals{i}, fs); end predictedLabels = test_gmm_hmm(model, testFeaturesCell); %% 7. 计算识别准确率 accuracy = sum(strcmp(predictedLabels, testLabels)) / length(testLabels) * 100; fprintf('>>> 测试集识别准确率：%.2f%%\n', accuracy); disp('>>> 语音识别流程结束！');