A new theoretical framework from Apple defines phonetic similarity through probabilistic distances in fixed-dimensional embedding spaces. The research proves uniform cluster-wise isotropy for variable-width audio and text representations. This mathematical foundation allows developers to interpret acoustic neighbor embeddings more predictably. It provides a principled method for mapping phonetic content in speech systems.