# Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

arXiv: https://arxiv.org/abs/1709.07902 · HTML: https://mlsanity.com/paper/1709.07902/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 30 of 93)

- [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)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [Contrastively Disentangled Sequential Variational Autoencoder](https://mlsanity.com/paper/2110.12091/) (2021)
- [An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild](https://mlsanity.com/paper/2106.15610/) (2021)
- [Textless Speech Emotion Conversion using Discrete and Decomposed Representations](https://mlsanity.com/paper/2111.07402/) (2021)
- [Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion](https://mlsanity.com/paper/2203.16705/) (2022)
- [Multifactor Sequential Disentanglement via Structured Koopman Autoencoders](https://mlsanity.com/paper/2303.17264/) (2023)
- [Disentangled Recurrent Wasserstein Autoencoder](https://mlsanity.com/paper/2101.07496/) (2021)
- [Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering](https://mlsanity.com/paper/2305.11072/) (2023)
- [Temporally Disentangled Representation Learning under Unknown Nonstationarity](https://mlsanity.com/paper/2310.18615/) (2023)
- [Decoupling Local and Global Representations of Time Series](https://mlsanity.com/paper/2202.02262/) (2022)
- [Scaling-up Disentanglement for Image Translation](https://mlsanity.com/paper/2103.14017/) (2021)
- [3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement](https://mlsanity.com/paper/2306.15354/) (2023)
- [A Contrastive Objective for Learning Disentangled Representations](https://mlsanity.com/paper/2203.11284/) (2022)
- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- [Modeling Inter-Intra Heterogeneity for Graph Federated Learning](https://mlsanity.com/paper/2412.11402/) (2024)
- [Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning](https://mlsanity.com/paper/2102.11327/) (2021)
- [Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech](https://mlsanity.com/paper/2208.05445/) (2022)
- [Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation](https://mlsanity.com/paper/2305.15924/) (2023)
- [A Brief Overview of Unsupervised Neural Speech Representation Learning](https://mlsanity.com/paper/2203.01829/) (2022)
- [Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder](https://mlsanity.com/paper/2107.06642/) (2021)
- [Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE](https://mlsanity.com/paper/2206.02512/) (2022)
- [Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing](https://mlsanity.com/paper/2211.01522/) (2022)
- [Sequential Disentanglement by Extracting Static Information From A Single Sequence Element](https://mlsanity.com/paper/2406.18131/) (2024)

## Abstract (arXiv)

We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables. The model is evaluated on two speech corpora to demonstrate, qualitatively, its ability to transform speakers or linguistic content by manipulating different sets of latent variables; and quantitatively, its ability to outperform an i-vector baseline for speaker verification and reduce the word error rate by as much as 35% in mismatched train/test scenarios for automatic speech recognition tasks.