FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912558468825088 |
|---|---|
| author | Tabatabaee, Saba Liu, Jing Espy-Wilson, Carol |
| author_facet | Tabatabaee, Saba Liu, Jing Espy-Wilson, Carol |
| contents | Creating Speaker Verification (SV) systems for classroom settings that are robust to classroom noises such as babble noise is crucial for the development of AI tools that assist educational environments. In this work, we study the efficacy of finetuning with augmented children datasets to adapt the x-vector and ECAPA-TDNN to classroom environments. We demonstrate that finetuning with augmented children's datasets is powerful in that regard and reduces the Equal Error Rate (EER) of x-vector and ECAPA-TDNN models for both classroom datasets and children speech datasets. Notably, this method reduces EER of the ECAPA-TDNN model on average by half (a 5 % improvement) for classrooms in the MPT dataset compared to the ECAPA-TDNN baseline model. The x-vector model shows an 8 % average improvement for classrooms in the NCTE dataset compared to its baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments Tabatabaee, Saba Liu, Jing Espy-Wilson, Carol Audio and Speech Processing Creating Speaker Verification (SV) systems for classroom settings that are robust to classroom noises such as babble noise is crucial for the development of AI tools that assist educational environments. In this work, we study the efficacy of finetuning with augmented children datasets to adapt the x-vector and ECAPA-TDNN to classroom environments. We demonstrate that finetuning with augmented children's datasets is powerful in that regard and reduces the Equal Error Rate (EER) of x-vector and ECAPA-TDNN models for both classroom datasets and children speech datasets. Notably, this method reduces EER of the ECAPA-TDNN model on average by half (a 5 % improvement) for classrooms in the MPT dataset compared to the ECAPA-TDNN baseline model. The x-vector model shows an 8 % average improvement for classrooms in the NCTE dataset compared to its baseline. |
| title | FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.20222 |