# Speaker-Utterance Dual Attention for Speaker and Utterance Verification

arXiv: https://arxiv.org/abs/2008.08901 · HTML: https://mlsanity.com/paper/2008.08901/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- [MFA: TDNN with Multi-scale Frequency-channel Attention for Text-independent Speaker Verification with Short Utterances](https://mlsanity.com/paper/2202.01624/) (2022)
- [PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction](https://mlsanity.com/paper/2110.00940/) (2021)
- [Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems](https://mlsanity.com/paper/2111.03842/) (2021)
- [Learning Decoupling Features Through Orthogonality Regularization](https://mlsanity.com/paper/2203.16772/) (2022)

## Abstract (arXiv)

In this paper, we study a novel technique that exploits the interaction between speaker traits and linguistic content to improve both speaker verification and utterance verification performance. We implement an idea of speaker-utterance dual attention (SUDA) in a unified neural network. The dual attention refers to an attention mechanism for the two tasks of speaker and utterance verification. The proposed SUDA features an attention mask mechanism to learn the interaction between the speaker and utterance information streams. This helps to focus only on the required information for respective task by masking the irrelevant counterparts. The studies conducted on RSR2015 corpus confirm that the proposed SUDA outperforms the framework without attention mask as well as several competitive systems for both speaker and utterance verification.