PCA 应用于 MFCC 以提供 GMM 分类器（sklearn 库）

Question

我面临一个（可能很简单）的问题，我必须使用 PCA 来降低特征向量的维数。所有这一切的要点是创建一个分类器来预测由音素组成的句子。我用人们发音的几个小时的句子（句子只有 10 个）来训练我的模型，每个句子都有一个由一组音素组成的标签（见下文）。

到目前为止我所做的如下：

import mdp
from sklearn import mixture
from features import mfcc

def extract_mfcc():
    X_train = []
    directory = test_audio_folder

    # Iterate through each .wav file and extract the mfcc
    for audio_file in glob.glob(directory):
        (rate, sig) = wav.read(audio_file)
        mfcc_feat = mfcc(sig, rate)

        X_train.append(mfcc_feat)
    return np.array(X_train)

def extract_labels():
    Y_train = []

    # here I have all the labels - each label is a sentence composed by a set of phonemes
    with open(labels_files) as f:
        for line in f:  # Ex: line = AH0 P IY1 S AH0 V K EY1 K
            Y_train.append(line)
        return np.array(Y_train)

def main():
   __X_train = extract_mfcc()
   Y_train = extract_labels()

   # Now, according to every paper I read, I need to reduce the dimensionality of my mfcc vector before to feed my gaussian mixture model
   
   X_test = []
   for feat in __X_train:
       pca = mdp.pca(feat)
       X_test.append(pca)

   n_classes = 10 # I'm trying to predict only 10 sentences (each sentence is composed by the phonemes described above)
   gmm_classifier = mixture.GMM(n_components=n_classes, covariance_type='full')
   gmm_classifier.fit(X_train)  # error here!reason: each "pca" that I appended before in X_train has a different shape (same number of columns though)

如何降低维度，同时使我提取的每个 PCA 具有相同的形状？

我还尝试了一项新事物：在 for 循环中调用 gmm_classifier.fit(...)，在其中获得 PCA 向量（请参阅下面的代码）。函数 fit() 有效，但我不确定我是否真的正确地训练了 GMM。

n_classes = 10
gmm_classifier = mixture.GMM(n_components=n_classes, covariance_type='full')

X_test = []
for feat in __X_train:
    pca = mdp.pca(feat)
    gmm_classifier.fit(pca) # in this way it works, but I'm not sure if it actually model is trained correctly

非常感谢

Answer 1

关于您最后的评论/问题： gmm_classifier.fit(pca) # 以这种方式它可以工作，但我不确定它实际上模型是否训练正确 每当您调用此方法时，分类器都会忘记以前的信息，并且仅根据最后的数据进行训练。尝试将壮举附加到循环内，然后进行拟合。