我是Matlab的新手并正在进行信号处理项目（语音识别）。在做了一些计算之后，我在矩阵中得到了一些称为MFCC（Mel-Frequency Cepstral Coefficient）的值。我现在应该使用函数gmdistribution.fit（X，k）来应用高斯混合模型（GMM）分布。但我一直收到错误，
X must have more rows than columns.
我不明白，我该如何解决这个问题？我尝试对矩阵进行转置，但接着，我得到了其他错误。
??? Error using ==> gmcluster at 181
Ill-conditioned covariance created at iteration 3.
Error in ==> gmdistribution.fit at 199
[S,NlogL,optimInfo] =...
我的MFCC矩阵通常有13行，大约50-80列。
有关如何解决此问题的任何想法？我一次最多只能使用12个字符吗？或者什么是备用期望最大化（EM）算法来获得语音识别中的最大似然（ML）估计？
这是从语音中提取mfcc特征向量后得到的样本矩阵：
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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答案 0 :(得分：1)
我不明白，我该如何解决这个问题？
你需要转置矩阵，你做对了。向量必须在原始
上有关如何解决此问题的任何想法？
GMDISTRIBUTION实现了标准的期望最大化（EM）算法。在一些情况下，它可以收敛到包含一个或多个分量的奇异或近似奇异协方差矩阵的解。这些组件通常包含几个几乎位于较低维子空间的数据点。具有奇异协方差矩阵的解是 通常被认为是虚假的。有时，如果您尝试另一组初始值，此问题可能会消失;有时，由于以下任何原因，总会发生此问题：
在您的情况下，您使用的组件数量8似乎太大了。您可以尝试减少组件数量。通常，您还可以使用其他方法来避免出现“病态协方差矩阵”错误消息“
另见
http://www.mathworks.com/matlabcentral/newsreader/view_thread/168289
我一次只能使用最多12列吗？
没有
答案 1 :(得分：1)
@Shark 我有同样的问题，试图从一组数据生成高斯MM对象。 我通过指定协方差的类型来解决它：
GMM1 = gmdistribution.fit（X，K， 'CovType'， '对角'）
GMM1是对象名称。你可以在
中找到X和k的含义帮助gmdistribution.fit
如果这对您不起作用，请尝试指定EM算法的初始值 gmdistribution已经用于生成GMM。
爱丽思
答案 2 :(得分：0)
你们中的第一个人应该把它变成线性的，因为它对于matlab来说太大了，之后最好只需要7-10个特征（我认为你得到的不仅仅是这个）。 在你完成你的工作后，然后使用重塑功能，使其成为你想要的