Interpreting hierarchical organisation of speaker embeddings
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arXiv:2604. 23354v3 Announce Type: replace-cross Abstract: Neural networks can be trained to learn task-relevant representations from data.
The paper introduces the concept of second‑order patterns—latent structures that underlie a speaker recognition network’s classification of utterances. Using hierarchical clustering, the authors identify these patterns and interpret them with the HCCM method. They then propose a new task, second‑order pattern recognition, and present the HCNA method, which improves performance by matching unseen utterances to the extrapolation space of identified clusters.
arXiv:2609.39162v1 Announce Type: cross Abstract: Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermed...
arXiv:2607. 05276v1 Announce Type: cross Abstract: Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather than generative: they map an observed speech segment to an x-vector, which is then used for downstream applications.
arXiv:2403.14830v2 Announce Type: replace Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensio...
arXiv:2405. 12775v2 Announce Type: replace-cross Abstract: Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions.