# Parameterized Channel Normalization for Far-field Deep Speaker Verification

arXiv: https://arxiv.org/abs/2109.12056 · HTML: https://mlsanity.com/paper/2109.12056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [The INTERSPEECH 2020 Far-Field Speaker Verification Challenge](https://mlsanity.com/paper/2005.08046/) (2020)
- [HI-MIA : A Far-field Text-Dependent Speaker Verification Database and the Baselines](https://mlsanity.com/paper/1912.01231/) (2019)
- [Learnable MFCCs for Speaker Verification](https://mlsanity.com/paper/2102.10322/) (2021)
- [A Comparative Re-Assessment of Feature Extractors for Deep Speaker Embeddings](https://mlsanity.com/paper/2007.15283/) (2020)

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

- [Learnable Nonlinear Compression for Robust Speaker Verification](https://mlsanity.com/paper/2202.05236/) (2022)

## Abstract (arXiv)

We address far-field speaker verification with deep neural network (DNN) based speaker embedding extractor, where mismatch between enrollment and test data often comes from convolutive effects (e.g. room reverberation) and noise. To mitigate these effects, we focus on two parametric normalization methods: per-channel energy normalization (PCEN) and parameterized cepstral mean normalization (PCMN). Both methods contain differentiable parameters and thus can be conveniently integrated to, and jointly optimized with the DNN using automatic differentiation methods. We consider both fixed and trainable (data-driven) variants of each method. We evaluate the performance on Hi-MIA, a recent large-scale far-field speech corpus, with varied microphone and positional settings. Our methods outperform conventional mel filterbank features, with maximum of 33.5% and 39.5% relative improvement on equal error rate under matched microphone and mismatched microphone conditions, respectively.