# Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding

arXiv: https://arxiv.org/abs/2011.12536 · HTML: https://mlsanity.com/paper/2011.12536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (2022)
- [On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification](https://mlsanity.com/paper/2201.06426/) (2022)
- [Vocal Tract Length Warped Features for Spoken Keyword Spotting](https://mlsanity.com/paper/2501.03523/) (2025)

## Abstract (arXiv)

In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training deep neural networks with a self-supervised objective, autoregressive predictive coding (APC), for TD-SV and compare it with the well-studied speaker-discriminant BN feature. The proposed VTL method is then applied to APC and speaker-discriminant BN features. In the end, we combine the VTL perturbation systems trained on MFCC and the two BN features in the score domain. Experiments are performed on the RedDots challenge 2016 database of TD-SV using short utterances with Gaussian mixture model-universal background model and i-vector techniques. Results show the proposed methods significantly outperform the baselines.