# CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations

arXiv: https://arxiv.org/abs/2210.02592 · HTML: https://mlsanity.com/paper/2210.02592/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [Data Augmenting Contrastive Learning of Speech Representations in the Time Domain](https://mlsanity.com/paper/2007.00991/) (2020)
- [UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training](https://mlsanity.com/paper/2110.05752/) (2021)
- [Solving Inefficiency of Self-supervised Representation Learning](https://mlsanity.com/paper/2104.08760/) (2021)
- [MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition](https://mlsanity.com/paper/2102.12664/) (2021)
- [Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning](https://mlsanity.com/paper/2010.13991/) (2020)
- [The History of Speech Recognition to the Year 2030](https://mlsanity.com/paper/2108.00084/) (2021)
- [Wav2Vec-Aug: Improved self-supervised training with limited data](https://mlsanity.com/paper/2206.13654/) (2022)
- [Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios](https://mlsanity.com/paper/2106.03419/) (2021)
- [Text Transformations in Contrastive Self-Supervised Learning: A Review](https://mlsanity.com/paper/2203.12000/) (2022)

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

- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning](https://mlsanity.com/paper/2305.10005/) (2023)
- [SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2210.08634/) (2022)
- [LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec](https://mlsanity.com/paper/2410.15764/) (2024)
- [The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2406.09494/) (2024)
- [data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setup](https://mlsanity.com/paper/2211.01246/) (2022)
- [StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs](https://mlsanity.com/paper/2509.22220/) (2025)
- [Convexity-based Pruning of Speech Representation Models](https://mlsanity.com/paper/2408.11858/) (2024)
- [Removing Speaker Information from Speech Representation using Variable-Length Soft Pooling](https://mlsanity.com/paper/2404.00856/) (2024)
- [SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis](https://mlsanity.com/paper/2308.01018/) (2023)

## Abstract (arXiv)

While Self-Supervised Learning has helped reap the benefit of the scale from the available unlabeled data, the learning paradigms are continuously being bettered. We present a new pre-training strategy named ccc-wav2vec 2.0, which uses clustering and an augmentation-based cross-contrastive loss as its self-supervised objective. Through the clustering module, we scale down the influence of those negative examples that are highly similar to the positive. The Cross-Contrastive loss is computed between the encoder output of the original sample and the quantizer output of its augmentation and vice-versa, bringing robustness to the pre-training strategy. ccc-wav2vec 2.0 achieves up to 15.6% and 12.7% relative WER improvement over the baseline wav2vec 2.0 on the test-clean and test-other sets, respectively, of LibriSpeech, without the use of any language model. The proposed method also achieves up to 14.9% relative WER improvement over the baseline wav2vec 2.0 when fine-tuned on Switchboard data. We make all our codes publicly available on GitHub.