# Removing Speaker Information from Speech Representation using Variable-Length Soft Pooling

arXiv: https://arxiv.org/abs/2404.00856 · HTML: https://mlsanity.com/paper/2404.00856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [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)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [The Zero Resource Speech Challenge 2021: Spoken language modelling](https://mlsanity.com/paper/2104.14700/) (2021)
- [Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation](https://mlsanity.com/paper/2007.13465/) (2020)
- [Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning](https://mlsanity.com/paper/2010.13991/) (2020)
- [Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation](https://mlsanity.com/paper/2106.02170/) (2021)
- [Variable-rate discrete representation learning](https://mlsanity.com/paper/2103.06089/) (2021)
- [Variable-rate hierarchical CPC leads to acoustic unit discovery in speech](https://mlsanity.com/paper/2206.02211/) (2022)
- [CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations](https://mlsanity.com/paper/2210.02592/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)

## Abstract (arXiv)

Recently, there have been efforts to encode the linguistic information of speech using a self-supervised framework for speech synthesis. However, predicting representations from surrounding representations can inadvertently entangle speaker information in the speech representation. This paper aims to remove speaker information by exploiting the structured nature of speech, composed of discrete units like phonemes with clear boundaries. A neural network predicts these boundaries, enabling variable-length pooling for event-based representation extraction instead of fixed-rate methods. The boundary predictor outputs a probability for the boundary between 0 and 1, making pooling soft. The model is trained to minimize the difference with the pooled representation of the data augmented by time-stretch and pitch-shift. To confirm that the learned representation includes contents information but is independent of speaker information, the model was evaluated with libri-light's phonetic ABX task and SUPERB's speaker identification task.