# Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification

arXiv: https://arxiv.org/abs/2008.03616 · HTML: https://mlsanity.com/paper/2008.03616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Learning from human perception to improve automatic speaker verification in style-mismatched conditions](https://mlsanity.com/paper/2206.13684/) (2022)
- [Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings](https://mlsanity.com/paper/2209.12602/) (2022)
- [Attention-based conditioning methods using variable frame rate for style-robust speaker verification](https://mlsanity.com/paper/2206.13680/) (2022)
- [Enhancing Video-Based Robot Failure Detection Using Task Knowledge](https://mlsanity.com/paper/2508.18705/) (2025)

## Abstract (arXiv)

The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles per speaker. An x-vector/PLDA (probabilistic linear discriminant analysis) system was trained with the SRE and Switchboard databases with standard augmentation techniques and evaluated with utterances from the UCLA database. The equal error rate (EER) was low when enrollment and test utterances were of the same style (e.g., 0.98% and 0.57% for read and conversational speech, respectively), but it increased substantially when styles were mismatched between enrollment and test utterances. For instance, when enrolled with conversation utterances, the EER increased to 3.03%, 2.96% and 22.12% when tested on read, narrative, and pet-directed speech, respectively. To reduce the effect of style mismatch, we propose an entropy-based variable frame rate technique to artificially generate style-normalized representations for PLDA adaptation. The proposed system significantly improved performance. In the aforementioned conditions, the EERs improved to 2.69% (conversation -- read), 2.27% (conversation -- narrative), and 18.75% (pet-directed -- read). Overall, the proposed technique performed comparably to multi-style PLDA adaptation without the need for training data in different speaking styles per speaker.