title: training UBM with sidekit from custom data
body: <p>I am trying to train GMM-UBM model from data that i have already extracted for emotion recognition with SIDEKIT(pretty much the same as speaker recognition. I also don't understand the HDF5 feature file system). My data is an ndarray with shape (1101,78) [78 are the number of acoustic features and 1101 the number of feature vectors(frames). </p>
  
  <pre><code>ubm = sidekit.Mixture()
  
  llks = ubm.EM_uniform(anger, distribNb, iteration_min=3, iteration_max=10, llk_gain=0.01, do_init=True)
  </code></pre>
  
  <p>The error that is thrown is: </p>
  
  <pre><code>line 394, in _compute_all
      self.A = (numpy.square(self.mu) * self.invcov).sum(1) - 2.0 * (numpy.log(self.w) + numpy.log(self.cst))
  
  ValueError: operands could not be broadcast together with shapes (512,78) (512,0)
  </code></pre>
  
  <p>which means that the covariance matrix is of shape (512,0). Is that wrong? Should it be like (512,78)? I may be wrong. Please give me a hint</p>