# wav2vec: Unsupervised Pre-training for Speech Recognition

arXiv: https://arxiv.org/abs/1904.05862 · HTML: https://mlsanity.com/paper/1904.05862/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

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

- [WavChat: A Survey of Spoken Dialogue Models](https://mlsanity.com/paper/2411.13577/) (2024)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026)
- [Foundations of Multisensory Artificial Intelligence](https://mlsanity.com/paper/2404.18976/) (2024)
- [Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods](https://mlsanity.com/paper/2308.00129/) (2023)
- [Sentiment Reasoning for Healthcare](https://mlsanity.com/paper/2407.21054/) (2024)
- [Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network](https://mlsanity.com/paper/2411.15082/) (2024)
- [Think-Before-Draw: Decomposing Emotion Semantics & Fine-Grained Controllable Expressive Talking Head Generation](https://mlsanity.com/paper/2507.12761/) (2025)

## Abstract (arXiv)

We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task. Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available. Our approach achieves 2.43% WER on the nov92 test set. This outperforms Deep Speech 2, the best reported character-based system in the literature while using two orders of magnitude less labeled training data.