# End-to-End Text-Dependent Speaker Verification

arXiv: https://arxiv.org/abs/1509.08062 · HTML: https://mlsanity.com/paper/1509.08062/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2015.

## Built on by (most cited first, 30 of 49)

- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [Textless Speech Emotion Conversion using Discrete and Decomposed Representations](https://mlsanity.com/paper/2111.07402/) (2021)
- [FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2312.04432/) (2023)
- [fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit](https://mlsanity.com/paper/2109.06912/) (2021)
- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Streaming Multi-talker Speech Recognition with Joint Speaker Identification](https://mlsanity.com/paper/2104.02109/) (2021)
- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- ["Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World](https://mlsanity.com/paper/2109.09598/) (2021)
- [Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition](https://mlsanity.com/paper/2104.01989/) (2021)
- [Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model](https://mlsanity.com/paper/2304.11976/) (2023)
- [What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis](https://mlsanity.com/paper/2107.00439/) (2021)
- [Massive Sound Embedding Benchmark (MSEB)](https://mlsanity.com/paper/2602.07143/) (2026)
- [Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation](https://mlsanity.com/paper/2308.06457/) (2023)
- [MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems](https://mlsanity.com/paper/2309.06981/) (2023)
- [RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text](https://mlsanity.com/paper/2405.20336/) (2024)
- [Dictionary Attacks on Speaker Verification](https://mlsanity.com/paper/2204.11304/) (2022)
- [Ultra-Low-Bitrate Speech Coding with Pretrained Transformers](https://mlsanity.com/paper/2207.02262/) (2022)
- [TinySV: Speaker Verification in TinyML with On-device Learning](https://mlsanity.com/paper/2406.01655/) (2024)
- [Serialized Multi-Layer Multi-Head Attention for Neural Speaker Embedding](https://mlsanity.com/paper/2107.06493/) (2021)
- [Domain Agnostic Few-shot Learning for Speaker Verification](https://mlsanity.com/paper/2206.13700/) (2022)
- [Label Aware Speech Representation Learning For Language Identification](https://mlsanity.com/paper/2306.04374/) (2023)
- [Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters](https://mlsanity.com/paper/2401.05111/) (2024)
- [Deep FlexQP: Accelerated Nonlinear Programming via Deep Unfolding](https://mlsanity.com/paper/2512.01565/) (2025)
- [Real Additive Margin Softmax for Speaker Verification](https://mlsanity.com/paper/2110.09116/) (2021)
- [Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection](https://mlsanity.com/paper/2211.09858/) (2022)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking](https://mlsanity.com/paper/2503.05794/) (2025)
- [Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning](https://mlsanity.com/paper/2102.05630/) (2021)

## Abstract (arXiv)

In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time. Such an approach will result in simple and efficient systems, requiring little domain-specific knowledge and making few model assumptions. We implement the idea by formulating the problem as a single neural network architecture, including the estimation of a speaker model on only a few utterances, and evaluate it on our internal "Ok Google" benchmark for text-dependent speaker verification. The proposed approach appears to be very effective for big data applications like ours that require highly accurate, easy-to-maintain systems with a small footprint.