# Attention-Based Models for Text-Dependent Speaker Verification

arXiv: https://arxiv.org/abs/1710.10470 · HTML: https://mlsanity.com/paper/1710.10470/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 17 of 17)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech](https://mlsanity.com/paper/2202.12163/) (2022)
- [Graph-based Label Propagation for Semi-Supervised Speaker Identification](https://mlsanity.com/paper/2106.08207/) (2021)
- [Multi-user VoiceFilter-Lite via Attentive Speaker Embedding](https://mlsanity.com/paper/2107.01201/) (2021)
- [Speech Rhythm-Based Speaker Embeddings Extraction from Phonemes and Phoneme Duration for Multi-Speaker Speech Synthesis](https://mlsanity.com/paper/2402.07085/) (2024)
- [Content-Aware Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2102.06467/) (2021)
- [The NPU System for the 2020 Personalized Voice Trigger Challenge](https://mlsanity.com/paper/2102.13552/) (2021)
- [Auto-KWS 2021 Challenge: Task, Datasets, and Baselines](https://mlsanity.com/paper/2104.00513/) (2021)
- [Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification](https://mlsanity.com/paper/2106.13514/) (2021)
- [Parameter-Free Attentive Scoring for Speaker Verification](https://mlsanity.com/paper/2203.05642/) (2022)
- [Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge](https://mlsanity.com/paper/2205.07083/) (2022)
- [Learning from human perception to improve automatic speaker verification in style-mismatched conditions](https://mlsanity.com/paper/2206.13684/) (2022)
- [Automatic Evaluation of Speaker Similarity](https://mlsanity.com/paper/2207.00344/) (2022)
- [Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions](https://mlsanity.com/paper/2104.11985/) (2021)
- [Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/2106.00052/) (2021)
- [Analyzing And Improving Neural Speaker Embeddings for ASR](https://mlsanity.com/paper/2301.04571/) (2023)

## Abstract (arXiv)

Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end text-dependent speaker recognition system. We explore different topologies and their variants of the attention layer, and compare different pooling methods on the attention weights. Ultimately, we show that attention-based models can improves the Equal Error Rate (EER) of our speaker verification system by relatively 14% compared to our non-attention LSTM baseline model.