# Analyzing autoencoder-based acoustic word embeddings

arXiv: https://arxiv.org/abs/2004.01647 · HTML: https://mlsanity.com/paper/2004.01647/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Crossmodal ASR Error Correction with Discrete Speech Units](https://mlsanity.com/paper/2405.16677/) (2024)
- [How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings](https://mlsanity.com/paper/2109.10179/) (2021)
- [A phonetic model of non-native spoken word processing](https://mlsanity.com/paper/2101.11332/) (2021)
- [Analyzing the Representational Geometry of Acoustic Word Embeddings](https://mlsanity.com/paper/2301.03012/) (2023)
- [Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations](https://mlsanity.com/paper/2403.08738/) (2024)
- [Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings](https://mlsanity.com/paper/2209.06633/) (2022)

## Abstract (arXiv)

Recent studies have introduced methods for learning acoustic word embeddings (AWEs)---fixed-size vector representations of words which encode their acoustic features. Despite the widespread use of AWEs in speech processing research, they have only been evaluated quantitatively in their ability to discriminate between whole word tokens. To better understand the applications of AWEs in various downstream tasks and in cognitive modeling, we need to analyze the representation spaces of AWEs. Here we analyze basic properties of AWE spaces learned by a sequence-to-sequence encoder-decoder model in six typologically diverse languages. We first show that these AWEs preserve some information about words' absolute duration and speaker. At the same time, the representation space of these AWEs is organized such that the distance between words' embeddings increases with those words' phonetic dissimilarity. Finally, the AWEs exhibit a word onset bias, similar to patterns reported in various studies on human speech processing and lexical access. We argue this is a promising result and encourage further evaluation of AWEs as a potentially useful tool in cognitive science, which could provide a link between speech processing and lexical memory.