  Deep learning architectures have made significant progress in terms of
performance in many research areas. The automatic speech recognition (ASR)
field has thus benefited from these scientific and technological advances,
particularly for acoustic modeling, now integrating deep neural network
architectures. However, these performance gains have translated into increased
complexity regarding the information learned and conveyed through these
black-box architectures. Following many researches in neural networks
interpretability, we propose in this article a protocol that aims to determine
which and where information is located in an ASR acoustic model (AM). To do so,
we propose to evaluate AM performance on a determined set of tasks using
intermediate representations (here, at different layer levels). Regarding the
performance variation and targeted tasks, we can emit hypothesis about which
information is enhanced or perturbed at different architecture steps.
Experiments are performed on both speaker verification, acoustic environment
classification, gender classification, tempo-distortion detection systems and
speech sentiment/emotion identification. Analysis showed that neural-based AMs
hold heterogeneous information that seems surprisingly uncorrelated with
phoneme recognition, such as emotion, sentiment or speaker identity. The
low-level hidden layers globally appears useful for the structuring of
information while the upper ones would tend to delete useless information for
phoneme recognition.
