# SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification

arXiv: https://arxiv.org/abs/2010.12653 · HTML: https://mlsanity.com/paper/2010.12653/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations](https://mlsanity.com/paper/2302.08137/) (2023)
- [Adapting TTS models For New Speakers using Transfer Learning](https://mlsanity.com/paper/2110.05798/) (2021)
- [ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis](https://mlsanity.com/paper/2203.10473/) (2022)
- [Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound](https://mlsanity.com/paper/2306.16022/) (2023)
- [SelfVC: Voice Conversion With Iterative Refinement using Self Transformations](https://mlsanity.com/paper/2310.09653/) (2023)
- [Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices](https://mlsanity.com/paper/2103.17142/) (2021)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)
- [Residual Information in Deep Speaker Embedding Architectures](https://mlsanity.com/paper/2302.02742/) (2023)
- [Evaluation of Speech Representations for MOS prediction](https://mlsanity.com/paper/2306.09979/) (2023)
- [Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/2106.00052/) (2021)
- [Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials](https://mlsanity.com/paper/2404.01981/) (2024)

## Abstract (arXiv)

We propose SpeakerNet - a new neural architecture for speaker recognition and speaker verification tasks. It is composed of residual blocks with 1D depth-wise separable convolutions, batch-normalization, and ReLU layers. This architecture uses x-vector based statistics pooling layer to map variable-length utterances to a fixed-length embedding (q-vector). SpeakerNet-M is a simple lightweight model with just 5M parameters. It doesn't use voice activity detection (VAD) and achieves close to state-of-the-art performance scoring an Equal Error Rate (EER) of 2.10% on the VoxCeleb1 cleaned and 2.29% on the VoxCeleb1 trial files.