# Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification

arXiv: https://arxiv.org/abs/2008.05695 · HTML: https://mlsanity.com/paper/2008.05695/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [AdvRush: Searching for Adversarially Robust Neural Architectures](https://mlsanity.com/paper/2108.01289/) (2021)
- [Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection](https://mlsanity.com/paper/2205.11738/) (2022)
- [CONetV2: Efficient Auto-Channel Size Optimization for CNNs](https://mlsanity.com/paper/2110.06830/) (2021)
- [EfficientTDNN: Efficient Architecture Search for Speaker Recognition](https://mlsanity.com/paper/2103.13581/) (2021)
- [DT-SV: A Transformer-based Time-domain Approach for Speaker Verification](https://mlsanity.com/paper/2205.13249/) (2022)
- [SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification](https://mlsanity.com/paper/2109.08839/) (2021)
- [QSpeech: Low-Qubit Quantum Speech Application Toolkit](https://mlsanity.com/paper/2205.13221/) (2022)
- [Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification](https://mlsanity.com/paper/2303.07643/) (2023)

## Abstract (arXiv)

State-of-the-art speaker verification models are based on deep learning techniques, which heavily depend on the handdesigned neural architectures from experts or engineers. We borrow the idea of neural architecture search(NAS) for the textindependent speaker verification task. As NAS can learn deep network structures automatically, we introduce the NAS conception into the well-known x-vector network. Furthermore, this paper proposes an evolutionary algorithm enhanced neural architecture search method called Auto-Vector to automatically discover promising networks for the speaker verification task. The experimental results demonstrate our NAS-based model outperforms state-of-the-art speaker verification models.