# Unsupervised Neural Hidden Markov Models

arXiv: https://arxiv.org/abs/1609.09007 · HTML: https://mlsanity.com/paper/1609.09007/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 19 of 19)

- [Sequence-to-Sequence Learning with Latent Neural Grammars](https://mlsanity.com/paper/2109.01135/) (2021)
- [PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols](https://mlsanity.com/paper/2104.13727/) (2021)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [Neural HMMs are all you need (for high-quality attention-free TTS)](https://mlsanity.com/paper/2108.13320/) (2021)
- [OverFlow: Putting flows on top of neural transducers for better TTS](https://mlsanity.com/paper/2211.06892/) (2022)
- [Prosody-controllable spontaneous TTS with neural HMMs](https://mlsanity.com/paper/2211.13533/) (2022)
- [Introducing the Hidden Neural Markov Chain framework](https://mlsanity.com/paper/2102.11038/) (2021)
- [On the Stochastic Stability of Deep Markov Models](https://mlsanity.com/paper/2111.04601/) (2021)
- [Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2205.14228/) (2022)
- [SynJax: Structured Probability Distributions for JAX](https://mlsanity.com/paper/2308.03291/) (2023)
- [Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging](https://mlsanity.com/paper/2203.10315/) (2022)
- [Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models](https://mlsanity.com/paper/2210.16659/) (2022)
- [Unsupervised Neural Hidden Markov Models with a Continuous latent state space](https://mlsanity.com/paper/2106.06536/) (2021)
- [Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?](https://mlsanity.com/paper/2206.14969/) (2022)
- [Review of Unsupervised POS Tagging and Its Implications on Language Acquisition](https://mlsanity.com/paper/2312.10169/) (2023)
- [Unsupervised Learning of Harmonic Analysis Based on Neural HSMM with Code Quality Templates](https://mlsanity.com/paper/2403.04135/) (2024)
- [Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure](https://mlsanity.com/paper/2406.03772/) (2024)
- [DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes](https://mlsanity.com/paper/2605.14632/) (2026)
- [Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences](https://mlsanity.com/paper/2607.12382/) (2026)

## Abstract (arXiv)

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.