Dyship: Leveraging Acoustic Features For Speaker Relationship Classification From Dyadic Conversations

arXiv:2611.14241v1 Announce Type: cross Abstract: We introduce DyShip, a framework for classifying speaker familiarity and kinship from dyadic conversations in the Seamless Interaction corpus. We compare five speech representations: spectrograms, MFCCs, self-supervised WavLM representations, x-vectors, and estimated articulatory movements. Spectral features perform best for familiarity classification, reaching an accuracy of 87.81%, while WavLM achieves the highest kinship accuracy of 85.17%. X-vectors and articulatory features yield weaker results on familiarity classifiaction, but shine for

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