# Improving the Performance of the LSTM and HMM Model via Hybridization

arXiv: https://arxiv.org/abs/1907.04670 · HTML: https://mlsanity.com/paper/1907.04670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [Hybrid hidden Markov LSTM for short-term traffic flow prediction](https://mlsanity.com/paper/2307.04954/) (2023)
- [On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models](https://mlsanity.com/paper/2609.26280/) (2026)

## Abstract (arXiv)

Language models based on deep neural networks and traditional stochastic modelling have become both highly functional and effective in recent times. In this work, a general survey into the two types of language modelling is conducted. We investigate the effectiveness of the Hidden Markov Model (HMM), and the Long Short-Term Memory Model (LSTM). We analyze the hidden state structures common to both models, and present an analysis on structural similarity of the hidden states, common to both HMM's and LSTM's. We compare the LSTM's predictive accuracy and hidden state output with respect to the HMM for a varying number of hidden states. In this work, we justify that the less complex HMM can serve as an appropriate approximation of the LSTM model.