# RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

arXiv: https://arxiv.org/abs/1904.08104 · HTML: https://mlsanity.com/paper/1904.08104/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 30 of 33)

- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [Preference Alignment Improves Language Model-Based TTS](https://mlsanity.com/paper/2409.12403/) (2024)
- [AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation](https://mlsanity.com/paper/2312.02512/) (2023)
- [Learning From Yourself: A Self-Distillation Method for Fake Speech Detection](https://mlsanity.com/paper/2303.01211/) (2023)
- [Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification](https://mlsanity.com/paper/2204.01005/) (2022)
- [Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection](https://mlsanity.com/paper/2104.03123/) (2021)
- [CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations](https://mlsanity.com/paper/2109.00181/) (2021)
- [Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies](https://mlsanity.com/paper/2408.06753/) (2024)
- [SVSNet: An End-to-end Speaker Voice Similarity Assessment Model](https://mlsanity.com/paper/2107.09392/) (2021)
- [Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data](https://mlsanity.com/paper/2204.04645/) (2022)
- [A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches](https://mlsanity.com/paper/2111.02041/) (2021)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [Statistics-aware Audio-visual Deepfake Detector](https://mlsanity.com/paper/2407.11650/) (2024)
- [Residual Information in Deep Speaker Embedding Architectures](https://mlsanity.com/paper/2302.02742/) (2023)
- [Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification](https://mlsanity.com/paper/2112.08929/) (2021)
- [Learnable Nonlinear Compression for Robust Speaker Verification](https://mlsanity.com/paper/2202.05236/) (2022)
- [Learning Decoupling Features Through Orthogonality Regularization](https://mlsanity.com/paper/2203.16772/) (2022)
- [Improved Relation Networks for End-to-End Speaker Verification and Identification](https://mlsanity.com/paper/2203.17218/) (2022)
- [The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.10147/) (2022)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [DeePen: Penetration Testing for Audio Deepfake Detection](https://mlsanity.com/paper/2502.20427/) (2025)
- [Traceable TTS: Toward Watermark-Free TTS with Strong Traceability](https://mlsanity.com/paper/2507.03887/) (2025)
- [Complementing Handcrafted Features with Raw Waveform Using a Light-weight Auxiliary Model](https://mlsanity.com/paper/2109.02773/) (2021)
- [Analyzing And Improving Neural Speaker Embeddings for ASR](https://mlsanity.com/paper/2301.04571/) (2023)
- [Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition](https://mlsanity.com/paper/2306.00952/) (2023)
- [Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)](https://mlsanity.com/paper/2412.00312/) (2024)
- [Improving speaker verification robustness with synthetic emotional utterances](https://mlsanity.com/paper/2412.00319/) (2024)
- [Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy](https://mlsanity.com/paper/2505.19951/) (2025)
- [Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework](https://mlsanity.com/paper/2508.02521/) (2025)

## Abstract (arXiv)

Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization of raw waveforms is in its preliminary phase, requiring further investigation. In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification. Adjustment of model architecture using a pre-training scheme can extract speaker embeddings, giving a significant improvement in performance. Additional objective functions simplify the process of extracting speaker embeddings by merging conventional two-phase processes: extracting utterance-level features such as i-vectors or x-vectors and the feature enhancement phase, e.g., linear discriminant analysis. Effective back-end classification models that suit the proposed speaker embedding are also explored. We propose an end-to-end system that comprises two deep neural networks, one front-end for utterance-level speaker embedding extraction and the other for back-end classification. Experiments conducted on the VoxCeleb1 dataset demonstrate that the proposed model achieves state-of-the-art performance among systems without data augmentation. The proposed system is also comparable to the state-of-the-art x-vector system that adopts data augmentation.