# Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition

arXiv: https://arxiv.org/abs/2207.03390 · HTML: https://mlsanity.com/paper/2207.03390/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages](https://mlsanity.com/paper/2005.08118/) (2020)
- [How Phonotactics Affect Multilingual and Zero-shot ASR Performance](https://mlsanity.com/paper/2010.12104/) (2020)

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

- [Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition](https://mlsanity.com/paper/2306.08577/) (2023)
- [Language-Specialized Multi-Teacher On-Policy Distillation for Multilingual LLM-Based ASR](https://mlsanity.com/paper/2608.03610/) (2026)
- [Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR](https://mlsanity.com/paper/2609.15758/) (2026)
- [MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition](https://mlsanity.com/paper/2310.18865/) (2023)
- [Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR](https://mlsanity.com/paper/2607.10256/) (2026)

## Abstract (arXiv)

Multilingual automatic speech recognition (ASR) systems mostly benefit low resource languages but suffer degradation in performance across several languages relative to their monolingual counterparts. Limited studies have focused on understanding the languages behaviour in the multilingual speech recognition setups. In this paper, a novel data-driven approach is proposed to investigate the cross-lingual acoustic-phonetic similarities. This technique measures the similarities between posterior distributions from various monolingual acoustic models against a target speech signal. Deep neural networks are trained as mapping networks to transform the distributions from different acoustic models into a directly comparable form. The analysis observes that the languages closeness can not be truly estimated by the volume of overlapping phonemes set. Entropy analysis of the proposed mapping networks exhibits that a language with lesser overlap can be more amenable to cross-lingual transfer, and hence more beneficial in the multilingual setup. Finally, the proposed posterior transformation approach is leveraged to fuse monolingual models for a target language. A relative improvement of ~8% over monolingual counterpart is achieved.