# Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition

arXiv: https://arxiv.org/abs/2106.10169 · HTML: https://mlsanity.com/paper/2106.10169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition](https://mlsanity.com/paper/1912.01679/) (2019)
- [Deep Speaker: an End-to-End Neural Speaker Embedding System](https://mlsanity.com/paper/1705.02304/) (2017)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Noise-tolerant Audio-visual Online Person Verification using an Attention-based Neural Network Fusion](https://mlsanity.com/paper/1811.10813/) (2018)
- [Unified Hypersphere Embedding for Speaker Recognition](https://mlsanity.com/paper/1807.08312/) (2018)
- [Few Shot Speaker Recognition using Deep Neural Networks](https://mlsanity.com/paper/1904.08775/) (2019)
- [SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System](https://mlsanity.com/paper/2104.02125/) (2021)

## Built on by (most cited first, 3 of 3)

- [On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification](https://mlsanity.com/paper/2201.06426/) (2022)
- [Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models](https://mlsanity.com/paper/2401.12440/) (2024)
- [Improving speaker verification robustness with synthetic emotional utterances](https://mlsanity.com/paper/2412.00319/) (2024)

## Abstract (arXiv)

By implicitly recognizing a user based on his/her speech input, speaker identification enables many downstream applications, such as personalized system behavior and expedited shopping checkouts. Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used. We wish to combine the advantages of both types of models through an ensemble system to make more reliable predictions. However, any such combined approach has to be robust to incomplete inputs, i.e., when either TD or TI input is missing. As a solution we propose a fusion of embeddings network foenet architecture, combining joint learning with neural attention. We compare foenet with four competitive baseline methods on a dataset of voice assistant inputs, and show that it achieves higher accuracy than the baseline and score fusion methods, especially in the presence of incomplete inputs.