# Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings

arXiv: https://arxiv.org/abs/1910.00341 · HTML: https://mlsanity.com/paper/1910.00341/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [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)
- [AdaMS: Deep Metric Learning with Adaptive Margin and Adaptive Scale for Acoustic Word Discrimination](https://mlsanity.com/paper/2210.14564/) (2022)
- [Relational Proxy Loss for Audio-Text based Keyword Spotting](https://mlsanity.com/paper/2406.05314/) (2024)
- [Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2505.16735/) (2025)
- [PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting](https://mlsanity.com/paper/2603.18023/) (2026)

## Abstract (arXiv)

Acoustic word embeddings --- fixed-dimensional vector representations of arbitrary-length words --- have attracted increasing interest in query-by-example spoken term detection. Recently, on the fact that the orthography of text labels partly reflects the phonetic similarity between the words' pronunciation, a multi-view approach has been introduced that jointly learns acoustic and text embeddings. It showed that it is possible to learn discriminative embeddings by designing the objective which takes text labels as well as word segments. In this paper, we propose a network architecture that expands the multi-view approach by combining the Siamese multi-view encoders with a shared decoder network to maximize the effect of the relationship between acoustic and text embeddings in embedding space. Discriminatively trained with multi-view triplet loss and decoding loss, our proposed approach achieves better performance on acoustic word discrimination task with the WSJ dataset, resulting in 11.1% relative improvement in average precision. We also present experimental results on cross-view word discrimination and word level speech recognition tasks.