# Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks

arXiv: https://arxiv.org/abs/2603.08231 · HTML: https://mlsanity.com/paper/2603.08231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer](https://mlsanity.com/paper/2110.14782/) (2021)
- [MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment](https://mlsanity.com/paper/2410.05873/) (2024)
- [ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality](https://mlsanity.com/paper/2510.22037/) (2025)
- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (2025)
- [Cross-lingual Text-independent Speaker Verification using Unsupervised Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1908.01447/) (2019)
- [Cross-lingual Speaker Verification with Deep Feature Learning](https://mlsanity.com/paper/1706.07861/) (2017)
- [Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?](https://mlsanity.com/paper/2403.16777/) (2024)
- [Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets](https://mlsanity.com/paper/2403.20056/) (2024)
- [Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2511.13368/) (2025)
- [Interference Matrix: Quantifying Cross-Lingual Interference in Transformer Encoders](https://mlsanity.com/paper/2508.02256/) (2025)
- [A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features](https://mlsanity.com/paper/2111.10776/) (2021)

## Built on by (most cited first, 1 of 1)

- [Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages](https://mlsanity.com/paper/2607.01161/) (2026)

## Abstract (arXiv)

Paralinguistic speech tasks are often considered relatively language-agnostic, as they rely on extralinguistic acoustic cues rather than lexical content. However, prior studies report performance degradation under cross-lingual conditions, indicating non-negligible language dependence. Still, these studies typically focus on isolated language pairs or task-specific settings, limiting comparability and preventing a systematic assessment of task-level language dependence. We introduce the Cross-Lingual Transfer Matrix (CLTM), a systematic method to quantify cross-lingual interactions between pairs of languages within a given task. We apply the CLTM to two paralinguistic tasks, gender identification and speaker verification, using a multilingual HuBERT-based encoder, to analyze how donor-language data affects target-language performance during fine-tuning. Our results reveal distinct transfer patterns across tasks and languages, reflecting systematic, language-dependent effects.