# Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification

arXiv: https://arxiv.org/abs/2102.02074 · HTML: https://mlsanity.com/paper/2102.02074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Generative Pre-Training for Speech with Autoregressive Predictive Coding](https://mlsanity.com/paper/1910.12607/) (2019)
- [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification](https://mlsanity.com/paper/1709.01703/) (2017)
- [Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan](https://mlsanity.com/paper/1912.06311/) (2019)
- [Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification](https://mlsanity.com/paper/1905.04554/) (2019)
- [Cross lingual transfer learning for zero-resource domain adaptation](https://mlsanity.com/paper/1910.02168/) (2019)
- [Data augmentation enhanced speaker enrollment for text-dependent speaker verification](https://mlsanity.com/paper/2007.08004/) (2020)

## Abstract (arXiv)

In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set with two methods: (i) transfer learning and (ii) training from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs and the output from each PP-DNN at frame-level is considered one new set of generated data. The generated data from each PP-DNN is then used for building a TD-SV system in contrast to the conventional method that considers only the evaluation data available. The proposed approach can be considered as the transformation of data to the pass-phrase specific space using a non-linear transformation learned by each PP-DNN. The method develops several TD-SV systems with the number equal to the number of PP-DNNs separately trained for each pass-phrases for the evaluation. Finally, the scores of the different TD-SV systems are fused for decision making. Experiments are conducted on the RedDots challenge 2016 database for TD-SV using short utterances. Results show that the proposed method improves the performance for both conventional cepstral feature and deep bottleneck feature using both Gaussian mixture model - universal background model (GMM-UBM) and i-vector framework.