# Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery

arXiv: https://arxiv.org/abs/1904.03876 · HTML: https://mlsanity.com/paper/1904.03876/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language](https://mlsanity.com/paper/2106.12834/) (2021)
- [Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition](https://mlsanity.com/paper/2201.11207/) (2022)
- [Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation](https://mlsanity.com/paper/2104.00994/) (2021)
- [Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings](https://mlsanity.com/paper/2106.04298/) (2021)
- [A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery](https://mlsanity.com/paper/2206.11706/) (2022)
- [Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network](https://mlsanity.com/paper/2207.14568/) (2022)
- [Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units](https://mlsanity.com/paper/2407.04652/) (2024)

## Abstract (arXiv)

This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our approach may be described by the following two steps procedure: first the model learns the notion of acoustic units from the labelled data and then the model uses its knowledge to find new acoustic units on the target language. We implement this process with the Bayesian Subspace Hidden Markov Model (SHMM), a model akin to the Subspace Gaussian Mixture Model (SGMM) where each low dimensional embedding represents an acoustic unit rather than just a HMM's state. The subspace is trained on 3 languages from the GlobalPhone corpus (German, Polish and Spanish) and the AUs are discovered on the TIMIT corpus. Results, measured in equivalent Phone Error Rate, show that this approach significantly outperforms previous HMM based acoustic units discovery systems and compares favorably with the Variational Auto Encoder-HMM.