  Variation in speech is often quantified by comparing phonetic transcriptions
of the same utterance. However, manually transcribing speech is time-consuming
and error prone. As an alternative, therefore, we investigate the extraction of
acoustic embeddings from several self-supervised neural models. We use these
representations to compute word-based pronunciation differences between
non-native and native speakers of English, and between Norwegian dialect
speakers. For comparison with several earlier studies, we evaluate how well
these differences match human perception by comparing them with available human
judgements of similarity. We show that speech representations extracted from a
specific type of neural model (i.e. Transformers) lead to a better match with
human perception than two earlier approaches on the basis of phonetic
transcriptions and MFCC-based acoustic features. We furthermore find that
features from the neural models can generally best be extracted from one of the
middle hidden layers than from the final layer. We also demonstrate that neural
speech representations not only capture segmental differences, but also
intonational and durational differences that cannot adequately be represented
by a set of discrete symbols used in phonetic transcriptions.
