# Neural Speaker Embeddings for Ultrasound-based Silent Speech Interfaces

arXiv: https://arxiv.org/abs/2106.04552 · HTML: https://mlsanity.com/paper/2106.04552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

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

- [Adaptation of Tongue Ultrasound-Based Silent Speech Interfaces Using Spatial Transformer Networks](https://mlsanity.com/paper/2305.19130/) (2023)
- [Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data](https://mlsanity.com/paper/2308.10636/) (2023)
- [Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data](https://mlsanity.com/paper/2503.02849/) (2025)

## Abstract (arXiv)

Articulatory-to-acoustic mapping seeks to reconstruct speech from a recording of the articulatory movements, for example, an ultrasound video. Just like speech signals, these recordings represent not only the linguistic content, but are also highly specific to the actual speaker. Hence, due to the lack of multi-speaker data sets, researchers have so far concentrated on speaker-dependent modeling. Here, we present multi-speaker experiments using the recently published TaL80 corpus. To model speaker characteristics, we adjusted the x-vector framework popular in speech processing to operate with ultrasound tongue videos. Next, we performed speaker recognition experiments using 50 speakers from the corpus. Then, we created speaker embedding vectors and evaluated them on the remaining speakers. Finally, we examined how the embedding vector influences the accuracy of our ultrasound-to-speech conversion network in a multi-speaker scenario. In the experiments we attained speaker recognition error rates below 3%, and we also found that the embedding vectors generalize nicely to unseen speakers. Our first attempt to apply them in a multi-speaker silent speech framework brought about a marginal reduction in the error rate of the spectral estimation step.