Given the fast development of analysis techniques for NLP and speech
 processing systems, few systematic studies have been conducted to
 compare the strengths and weaknesses of each method.  As a step in
 this direction we study the case of representations of phonology in
 neural network models of spoken language. We use two commonly applied
 analytical techniques, diagnostic classifiers and representational
 similarity analysis, to quantify to what extent neural activation
 patterns encode phonemes and phoneme sequences. We manipulate two
 factors that can affect the outcome of analysis. First, we investigate
 the role of learning by comparing neural activations extracted from
 trained versus randomly-initialized models. Second, we examine the
 temporal scope of the activations by probing both local activations
 corresponding to a few milliseconds of the speech signal, and global
 activations pooled over the whole utterance. We conclude that
 reporting analysis results with randomly initialized models is
 crucial, and that global-scope methods tend to yield more consistent
 and interpretable results and we recommend their use as a complement
 to local-scope diagnostic methods.
We study representations of phonology in neural network models of spoken language with several variants of analytical techniques.