(7) Explainability: Explainability remains elusive to these large networks as they grow. Re-
searchers are steadfast in explaining these networks’ functioning and learning dynamics.
Recently, much work has been done to learn the fine-tuning and in-context learning dy-
namics of these large models for text under the neural-tangent-kernel (NTK) asymptotic
framework [366]. Such exploration is yet to be done in the speech domain. More yet, ex-
plainability could be built-in as inductive bias in architecture. To this end, brain-inspired
architectures [382] are being developed, which may shed more light on this aspect of large
models.
AReviewofDeepLearningTechniquesforSpeechProcessing