基于HMM的非特定人语音识别系统matlab代码

由于HMM非特定人语音识别系统代码比较复杂，这里提供一些参考代码和步骤，希望对你有所帮助。

步骤1：读取语音信号并进行预处理

% 读取语音信号
[x, fs] = audioread('speech.wav');

% 预处理
% 前端处理包括预加重、分帧、加窗、短时傅里叶变换等
preemph = [1, -0.97];
x = filter(preemph, 1, x);
frame_len = 0.025; % 帧长
frame_shift = 0.01; % 帧移
win = hamming(round(frame_len * fs));
nfft = 1024;
nframes = fix((length(x) - nfft + frame_shift * fs) / (frame_shift * fs));
frames = zeros(nfft, nframes);
for i = 1:nframes
    start = round((i - 1) * frame_shift * fs) + 1;
    frames(:, i) = x(start:start + nfft - 1) .* win;
end
stft = abs(fft(frames, nfft));

步骤2：训练HMM模型

% 状态数
nstates = 5;

% 混合高斯模型数
nmix = 8;

% 特征维度
ndim = size(stft, 1);

% 训练数据
train_data = stft(:, 1:200);

% 初始化HMM模型
prior = normalise(rand(nstates, 1));
transmat = mk_stochastic(rand(nstates, nstates));
mu = randn(ndim, nmix, nstates);
Sigma = repmat(eye(ndim), [1, 1, nmix, nstates]);
mixmat = mk_stochastic(rand(nmix, nstates));

% 训练HMM模型
[LL, prior, transmat, mu, Sigma, mixmat] = mhmm_em(train_data, prior, transmat, mu, Sigma, mixmat, 'max_iter', 50);

步骤3：识别语音信号

% 测试数据
test_data = stft(:, 201:end);

% HMM识别
loglik = mhmm_logprob(test_data, prior, transmat, mu, Sigma, mixmat);
[~, path] = viterbi_path(loglik, log(transmat), 'relative', true);

% 转换为音素序列
phoneme_seq = path2phoneme(path, fs, frame_len, frame_shift);

参考代码：

1. [HMM-based Speech Recognition in MATLAB](https://github.com/didiladi/matlab-speech-recognition)
2. [Hidden Markov Models for Speech Recognition in MATLAB](https://www.mathworks.com/help/signal/examples/hidden-markov-models-for-speech-recognition.html)