# End-to-End Attention based Text-Dependent Speaker Verification

arXiv: https://arxiv.org/abs/1701.00562 · HTML: https://mlsanity.com/paper/1701.00562/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2017.

## Built on by (most cited first, 7 of 7)

- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess](https://mlsanity.com/paper/2208.01366/) (2022)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing](https://mlsanity.com/paper/2202.09950/) (2022)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [Attention-based conditioning methods using variable frame rate for style-robust speaker verification](https://mlsanity.com/paper/2206.13680/) (2022)
- [Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report](https://mlsanity.com/paper/2605.14896/) (2026)

## Abstract (arXiv)

A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows 10 "Hey Cortana" speaker verification task.