Neural Network Training Variations in Speech and Subsequent Performance Evaluation

Ewout van den Berg, Bhuvana Ramabhadran, Michael Picheny

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • Abstract: In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first paper that performs an extensive empirical study on its effects in speech recognition. We view training as sampling from a distribution and show that these distributions can have a substantial variance. These observations have important implications on way results in the literature are reported and interpreted.
  • Conflicts: us.ibm.com

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