# Entropy-aware logistic regression for fusion of large-scale speaker recognition systems

arXiv: https://arxiv.org/abs/2609.23727 · HTML: https://mlsanity.com/paper/2609.23727/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 13 of 13)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Reshape Dimensions Network for Speaker Recognition](https://mlsanity.com/paper/2407.18223/) (2024)
- [ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings](https://mlsanity.com/paper/2401.08342/) (2024)
- [Xi-Vector Embedding for Speaker Recognition](https://mlsanity.com/paper/2108.05679/) (2021)
- [A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database](https://mlsanity.com/paper/1912.03627/) (2019)
- [Probabilistic embeddings for speaker diarization](https://mlsanity.com/paper/2004.04096/) (2020)
- [Studying squeeze-and-excitation used in CNN for speaker verification](https://mlsanity.com/paper/2109.05977/) (2021)
- [TidyVoice 2026 Challenge Evaluation Plan](https://mlsanity.com/paper/2601.21960/) (2026)
- [Likelihood-ratio calibration using prior-weighted proper scoring rules](https://mlsanity.com/paper/1307.7981/) (2013)
- [Xi+: Uncertainty Supervision for Robust Speaker Embedding](https://mlsanity.com/paper/2509.05993/) (2025)
- [Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification](https://mlsanity.com/paper/2606.22369/) (2026)

## Abstract (arXiv)

Score-level fusion based on logistic regression is widely used in speaker recognition to combine complementary systems. However, conventional approaches assign fixed system-dependent coefficients and do not explicitly account for variations in the reliability of individual enrollment and test utterances. Drawing on recent research on the entropy of deep learning-based speaker recognition models, this study incorporates an uncertainty component into the fusion process. By exploiting both system-level complementarity and utterance-dependent uncertainty, the method achieves robust performance in large-scale speaker recognition tasks that involve highly variable characteristics of the speech signal. These results demonstrate that model-entropy information provides a valuable complementary cue in large-scale scenarios.