# Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition

arXiv: https://arxiv.org/abs/2303.03657 · HTML: https://mlsanity.com/paper/2303.03657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Wav2Arrest 2.0: Long-Horizon Cardiac Arrest Prediction with Time-to-Event Modeling, Identity-Invariance, and Pseudo-Lab Alignment](https://mlsanity.com/paper/2509.21695/) (2025)
- [Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition](https://mlsanity.com/paper/2603.23057/) (2026)

## Abstract (arXiv)

Speech super-resolution/Bandwidth Extension (BWE) can improve downstream tasks like Automatic Speaker Verification (ASV). We introduce a simple novel technique called Self-FiLM to inject self-supervision into existing BWE models via Feature-wise Linear Modulation. We hypothesize that such information captures domain/environment information, which can give zero-shot generalization. Self-FiLM Conditional GAN (CGAN) gives 18% relative improvement in Equal Error Rate and 8.5% in minimum Decision Cost Function using state-of-the-art ASV system on SRE21 test. We further by 1) deep feature loss from time-domain models and 2) re-training of data2vec 2.0 models on naturalistic wideband (VoxCeleb) and telephone data (SRE Superset etc.). Lastly, we integrate self-supervision with CycleGAN to present a completely unsupervised solution that matches the semi-supervised performance.