# Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification

arXiv: https://arxiv.org/abs/2104.02370 · HTML: https://mlsanity.com/paper/2104.02370/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2021.

## Built on by (most cited first, 11 of 11)

- [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)
- [Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification](https://mlsanity.com/paper/2204.01005/) (2022)
- [Histogram Layer Time Delay Neural Networks for Passive Sonar Classification](https://mlsanity.com/paper/2307.13788/) (2023)
- [MACCIF-TDNN: Multi aspect aggregation of channel and context interdependence features in TDNN-based speaker verification](https://mlsanity.com/paper/2107.03104/) (2021)
- [CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization](https://mlsanity.com/paper/2110.13465/) (2021)
- [Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification](https://mlsanity.com/paper/2402.19355/) (2024)
- [Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction](https://mlsanity.com/paper/2506.10930/) (2025)
- [Auditory Intelligence: Understanding the World Through Sound](https://mlsanity.com/paper/2508.07829/) (2025)
- [Dynamic Kernels and Channel Attention for Low Resource Speaker Verification](https://mlsanity.com/paper/2211.02000/) (2022)
- [GMM-ResNext: Combining Generative and Discriminative Models for Speaker Verification](https://mlsanity.com/paper/2407.03135/) (2024)

## Abstract (arXiv)

This paper describes the IDLab submission for the text-independent task of the Short-duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker verification competition focuses on short duration test recordings and cross-lingual trials, along with the constraint of limited availability of in-domain DeepMine Farsi training data. Currently, both Time Delay Neural Networks (TDNNs) and ResNets achieve state-of-the-art results in speaker verification. These architectures are structurally very different and the construction of hybrid networks looks a promising way forward. We introduce a 2D convolutional stem in a strong ECAPA-TDNN baseline to transfer some of the strong characteristics of a ResNet based model to this hybrid CNN-TDNN architecture. Similarly, we incorporate absolute frequency positional encodings in an SE-ResNet34 architecture. These learnable feature map biases along the frequency axis offer this architecture a straightforward way to exploit frequency positional information. We also propose a frequency-wise variant of Squeeze-Excitation (SE) which better preserves frequency-specific information when rescaling the feature maps. Both modified architectures significantly outperform their corresponding baseline on the SdSVC-21 evaluation data and the original VoxCeleb1 test set. A four system fusion containing the two improved architectures achieved a third place in the final SdSVC-21 Task 2 ranking.