# Learnable Nonlinear Compression for Robust Speaker Verification

arXiv: https://arxiv.org/abs/2202.05236 · HTML: https://mlsanity.com/paper/2202.05236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [On the linearity of large non-linear models: when and why the tangent kernel is constant](https://mlsanity.com/paper/2010.01092/) (2020)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016)
- [RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification](https://mlsanity.com/paper/1904.08104/) (2019)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/1912.02522/) (2019)
- [VoiceID Loss: Speech Enhancement for Speaker Verification](https://mlsanity.com/paper/1904.03601/) (2019)
- [Playing a Part: Speaker Verification at the Movies](https://mlsanity.com/paper/2010.15716/) (2020)
- [Learnable MFCCs for Speaker Verification](https://mlsanity.com/paper/2102.10322/) (2021)
- [NPLDA: A Deep Neural PLDA Model for Speaker Verification](https://mlsanity.com/paper/2002.03562/) (2020)
- [Towards Robust Speaker Verification with Target Speaker Enhancement](https://mlsanity.com/paper/2103.08781/) (2021)
- [Sound Event Detection in Urban Audio With Single and Multi-Rate PCEN](https://mlsanity.com/paper/2102.03468/) (2021)
- [Parameterized Channel Normalization for Far-field Deep Speaker Verification](https://mlsanity.com/paper/2109.12056/) (2021)

## Built on by (most cited first, 1 of 1)

- [One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification](https://mlsanity.com/paper/2305.17394/) (2023)

## Abstract (arXiv)

In this study, we focus on nonlinear compression methods in spectral features for speaker verification based on deep neural network. We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner. Our methods are based on power nonlinearities and dynamic range compression (DRC). We also propose multi-regime (MR) design on the nonlinearities, at improving robustness. Results on VoxCeleb1 and VoxMovies data demonstrate improvements brought by proposed compression methods over both the commonly-used logarithm and their static counterparts, especially for ones based on power function. While CD generalization improves performance on VoxCeleb1, MR provides more robustness on VoxMovies, with a maximum relative equal error rate reduction of 21.6%.