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In recent times, BERT based transformer models have become an inseparablepart of the 'tech stack' of text processing models. Similar progress is beingobserved in the speech domain with a multitude of models observingstate-of-the-art results by using audio transformer models to encode speech.This begs the question of what are these audio transformer models learning.Moreover, although the standard methodology is to choose the last layerembedding for any downstream task, but is it the optimal choice? We try toanswer these questions for the two recent audio transformer models, Mockingjayand wave2vec2.0. We compare them on a comprehensive set of language deliveryand structure features including audio, fluency and pronunciation features.Additionally, we probe the audio models' understanding of textual surface,syntax, and semantic features and compare them to BERT. We do this overexhaustive settings for native, non-native, synthetic, read and spontaneousspeech datasets

What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure