MATLAB mfcc gmdistribution適合語音識別程序

Question

我是Matlab的新手，正在做一個信號處理項目（語音識別）。 在做了一些計算之后，我在矩陣中得到了一些稱為MFCC（Mel-Frequency Cepstral Coefficient）的值。 我現在應該使用函數gmdistribution.fit（X，k）來應用高斯混合模型（GMM）分布。 但我一直得到錯誤，

X must have more rows than columns.

我不明白，我該怎么辦呢？ 我試着對矩陣進行轉置，但后來我得到了其他錯誤。

??? Error using ==> gmcluster at 181
Ill-conditioned covariance created at iteration 3.

Error in ==> gmdistribution.fit at 199
    [S,NlogL,optimInfo] =...

我的MFCC矩陣通常有13行和約50-80列。

有想法該怎么解決這個嗎？ 我一次最多只能使用12個字符嗎？ 或者什么是備用期望最大化（EM）算法來獲得語音識別中的最大似然（ML）估計？

這是從語音中提取mfcc特征向量后得到的樣本矩陣：

 53.19162380493035  53.04536473593154   52.52404588266867   52.76558091790412   53.63907256262721   53.357790132994836  52.73205096524416   52.902995065027056  52.61096061282659   54.15474467851871   53.67444472478125   52.64177726437717   52.51697384592561   52.71137919365186   53.092851922453896  53.16427640450918   54.43019514688636   60.79640902129941   59.84919922646779   63.15389910551327   61.88723594060794   64.74826830389657   64.8349874832628    64.86278444375218   65.76126193531795   65.64589407152897   65.46920375829764   65.69178734432299   65.28831375816117   64.56074008418904   63.4966945660873    63.81859800557705   63.72800219675504   62.48994205815299   62.170438508902436  61.06563184036766   59.13583014975035   58.81335869501639   56.32130498897641   55.13711899166046   54.013505531107796  54.15759852717166   53.44176740036524   53.13219768600348   53.03407270007307   52.88271825256845   53.822163186509016  53.53892778841879   54.04538463287215   59.485371756367954  58.48009762761471   54.643413468895346  52.808848460884654  52.87392859698496   52.42111841679119   53.2365666558251    53.30622484832905   53.1799318016215    53.784807994410315  53.248067707554924  52.69122098296521   52.50131276155125   53.43030515391315   53.902384536061604  54.029570128176985  52.842675820980034  52.79731975873874   53.18695701339912
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Answer 1

我不明白，我該怎么辦呢？

你需要轉置矩陣，你做對了。 向量必須在原始數據上

有想法該怎么解決這個嗎？

GMDISTRIBUTION實現標准的期望最大化（EM）算法。 在一些情況下，它可以收斂到包含一個或多個分量的奇異或近似奇異協方差矩陣的解。 這些組件通常包含幾個幾乎位於較低維子空間的數據點。 具有奇異協方差矩陣的解通常被認為是偽的。 有時，如果您嘗試另一組初始值，此問題可能會消失; 有時，由於以下任何原因，總會發生此問題：

• 數據的維數相對較高，但沒有足夠的觀察結果。
• 您數據的某些功能（變量）具有高度相關性。
• 部分或全部功能是離散的。
• 您嘗試將數據擬合到太多組件。

在您的情況下，您使用的組件數量8似乎太大了。 您可以嘗試減少組件數量。 通常，您還可以使用其他方法來避免出現“病態協方差矩陣”錯誤消息“

1. 如果您不介意使用病態協方差矩陣獲得解決方案，可以在GMDISTRIBUTION / FIT函數中使用選項'Regularize'，在每個協方差矩陣的對角線上添加一個非常小的正數。
2. 您可以將'SharedCov'的值指定為true，以便為每個組件使用相等的協方差矩陣。
3. 您可以將'CovType'的值指定為'diagonal'。

也可以看看

http://www.mathworks.com/matlabcentral/newsreader/view_thread/168289

我一次最多只能使用12個字符嗎？

沒有

Answer 2

@Shark我有同樣的問題，試圖從一組數據生成一個高斯MM對象。 我通過指定協方差的類型來解決它：

GMM1 = gmdistribution.fit（X，K， 'CovType'， '對角'）

GMM1是對象名稱。 你可以找到X和k的含義

幫助gmdistribution.fit

如果這對您不起作用，請嘗試指定gmdistribution已用於生成GMM的EM算法的初始值。

愛麗思

Answer 3

你們中的第一個應該讓它變得線性，因為它對於matlab來說太大了，之后最好只需要7-10個特征（我認為你得到的不僅僅是這個）。 在你完成你的工作后，然后使用重塑功能，以使它成為你想要的