Triage knowledge distillation for speaker verification
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908779432378368 |
|---|---|
| author | Kim, Ju-ho Jung, Youngmoon Yang, Joon-Young Roh, Jaeyoung Han, Chang Woo Cho, Hoon-Young |
| author_facet | Kim, Ju-ho Jung, Youngmoon Yang, Joon-Young Roh, Jaeyoung Han, Chang Woo Cho, Hoon-Young |
| contents | Deploying speaker verification on resource-constrained devices remains challenging due to the computational cost of high-capacity models; knowledge distillation (KD) offers a remedy. Classical KD entangles target confidence with non-target structure in a Kullback-Leibler term, limiting the transfer of relational information. Decoupled KD separates these signals into target and non-target terms, yet treats non-targets uniformly and remains vulnerable to the long tail of low-probability classes in large-class settings. We introduce Triage KD (TRKD), a distillation scheme that operationalizes assess-prioritize-focus. TRKD introduces a cumulative-probability cutoff $τ$ to assess per-example difficulty and partition the teacher posterior into three groups: the target class, a high-probability non-target confusion-set, and a background-set. To prioritize informative signals, TRKD distills the confusion-set conditional distribution and discards the background. Concurrently, it transfers a three-mass (target/confusion/background) that capture sample difficulty and inter-class confusion. Finally, TRKD focuses learning via a curriculum on $τ$: training begins with a larger $τ$ to convey broad non-target context, then $τ$ is progressively decreased to shrink the confusion-set, concentrating supervision on the most confusable classes. In extensive experiments on VoxCeleb1 with both homogeneous and heterogeneous teacher-student pairs, TRKD was consistently superior to recent KD variants and attained the lowest EER across all protocols. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Triage knowledge distillation for speaker verification Kim, Ju-ho Jung, Youngmoon Yang, Joon-Young Roh, Jaeyoung Han, Chang Woo Cho, Hoon-Young Audio and Speech Processing Deploying speaker verification on resource-constrained devices remains challenging due to the computational cost of high-capacity models; knowledge distillation (KD) offers a remedy. Classical KD entangles target confidence with non-target structure in a Kullback-Leibler term, limiting the transfer of relational information. Decoupled KD separates these signals into target and non-target terms, yet treats non-targets uniformly and remains vulnerable to the long tail of low-probability classes in large-class settings. We introduce Triage KD (TRKD), a distillation scheme that operationalizes assess-prioritize-focus. TRKD introduces a cumulative-probability cutoff $τ$ to assess per-example difficulty and partition the teacher posterior into three groups: the target class, a high-probability non-target confusion-set, and a background-set. To prioritize informative signals, TRKD distills the confusion-set conditional distribution and discards the background. Concurrently, it transfers a three-mass (target/confusion/background) that capture sample difficulty and inter-class confusion. Finally, TRKD focuses learning via a curriculum on $τ$: training begins with a larger $τ$ to convey broad non-target context, then $τ$ is progressively decreased to shrink the confusion-set, concentrating supervision on the most confusable classes. In extensive experiments on VoxCeleb1 with both homogeneous and heterogeneous teacher-student pairs, TRKD was consistently superior to recent KD variants and attained the lowest EER across all protocols. |
| title | Triage knowledge distillation for speaker verification |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.14699 |