# Brain-inspired probabilistic generative model for double articulation analysis of spoken language

arXiv: https://arxiv.org/abs/2207.02457 · HTML: https://mlsanity.com/paper/2207.02457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models](https://mlsanity.com/paper/1910.08918/) (2019)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals](https://mlsanity.com/paper/1506.06646/) (2015)
- [The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain](https://mlsanity.com/paper/2103.06123/) (2021)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Unsupervised Phoneme and Word Discovery from Multiple Speakers using Double Articulation Analyzer and Neural Network with Parametric Bias](https://mlsanity.com/paper/1906.11049/) (2019)
- [Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues](https://mlsanity.com/paper/2201.06786/) (2022)

## Abstract (arXiv)

The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in which words are connected to form a sentence and words are composed of phonemes or syllables is called a double articulation structure. Where and how DAA is performed in the human brain has not been established, although some insights have been obtained. In addition, existing computational models based on a probabilistic generative model (PGM) do not incorporate neuroscientific findings, and their consistency with the brain has not been previously discussed. This study compared, mapped, and integrated these existing computational models with neuroscientific findings to bridge this gap, and the findings are relevant for future applications and further research. This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys. The study involved (i) investigation and organization of anatomical structures related to spoken language processing, and (ii) design of a PGM that matches the anatomy and functions of the region of interest. Therefore, this study provides novel insights that will be foundational to further exploring DAA in the brain.