# A phonetic model of non-native spoken word processing

arXiv: https://arxiv.org/abs/2101.11332 · HTML: https://mlsanity.com/paper/2101.11332/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units](https://mlsanity.com/paper/2010.05967/) (2020)
- [Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models](https://mlsanity.com/paper/1811.00403/) (2018)
- [Encoding of phonology in a recurrent neural model of grounded speech](https://mlsanity.com/paper/1706.03815/) (2017)
- [Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval](https://mlsanity.com/paper/1807.08089/) (2018)
- [Improved acoustic word embeddings for zero-resource languages using multilingual transfer](https://mlsanity.com/paper/2006.02295/) (2020)
- [Multilingual acoustic word embedding models for processing zero-resource languages](https://mlsanity.com/paper/2002.02109/) (2020)
- [Analyzing autoencoder-based acoustic word embeddings](https://mlsanity.com/paper/2004.01647/) (2020)
- [Evaluating computational models of infant phonetic learning across languages](https://mlsanity.com/paper/2008.02888/) (2020)

## Built on by (most cited first, 5 of 5)

- [Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study](https://mlsanity.com/paper/2106.08686/) (2021)
- [How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings](https://mlsanity.com/paper/2109.10179/) (2021)
- [Analyzing the Representational Geometry of Acoustic Word Embeddings](https://mlsanity.com/paper/2301.03012/) (2023)
- [Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings](https://mlsanity.com/paper/2209.06633/) (2022)
- [Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?](https://mlsanity.com/paper/2508.21210/) (2025)

## Abstract (arXiv)

Non-native speakers show difficulties with spoken word processing. Many studies attribute these difficulties to imprecise phonological encoding of words in the lexical memory. We test an alternative hypothesis: that some of these difficulties can arise from the non-native speakers' phonetic perception. We train a computational model of phonetic learning, which has no access to phonology, on either one or two languages. We first show that the model exhibits predictable behaviors on phone-level and word-level discrimination tasks. We then test the model on a spoken word processing task, showing that phonology may not be necessary to explain some of the word processing effects observed in non-native speakers. We run an additional analysis of the model's lexical representation space, showing that the two training languages are not fully separated in that space, similarly to the languages of a bilingual human speaker.