This work presents DonorRank, a learning-to-rank framework for predicting effective donor languages for zero-shot ASR and evaluates it on two multilingual speech corpora of Indic and African language families, finding transfer patterns that provide practical guidance for multilingual ASR in low-resource settings.
Abstract
Low-resource automatic speech recognition (ASR) commonly relies on cross-lingual transfer, where models are adapted from higher-resource donor languages. However, selecting donors remains challenging for spontaneous speech from under-resourced language communities, due to linguistic variation, evolving orthographic conventions, and uneven resource availability. We present DonorRank, a learning-to-rank framework for predicting effective donor languages for zero-shot ASR. We evaluate DonorRank on two multilingual speech corpora of Indic and African language families. It accurately predicts donor language rankings and improves donor selection over common heuristics based on genetic similarity or high-resource languages. Beyond improving transfer, we show how DonorRank is a general framework for analyzing donor language selection itself. Our analyses show that the composition of the donor set determines which linguistic cues are useful in predicting successful transfer. We also identify transfer patterns that provide practical guidance for multilingual ASR in low-resource settings.
Correlation analysis indicates that acoustic similarity is the strongest predictor of fine-tuning performance, while phoneme inventory and typological similarity better explain zero-shot transfer.
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Empirical analyses show that MoSE improves high-, medium-, and low-resource languages simultaneously, with the largest gains on low-resource speech, thereby breaking the curse of multilinguality without compromising high-resource performance.
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Detecting hate speech in low-resource and unseen languages remains challenging due to limited labeled data and linguistic diversity. This paper presents a comparative study of zero-shot cross-lingual transfer for hate speech detection using two multilingual transformer models: mDeBERTa-v3 and XLM-RoBERTa. To the best of our knowledge, mDeBERTa-v3 has not been previously used by researchers for zero-shot cross-lingual hate speech detection, making this the first study to evaluate its capabilities in this task. Furthermore, we introduce new unseen languages that have not been studied before in this context, including Hebrew, Amharic, and Swahili, alongside other languages such as Indonesian, Danish Portuguese, Turkish, French, and Russian. We evaluate model performance under three training scenarios: a single source language (Turkish), semantically similar language clusters, and multiple clusters from different language families. Experimental results show that mDeBERTa-v3 consistently outperforms XLM-RoBERTa in zero-shot settings. The most notable improvement is observed for Hebrew, where the macro F1 score increases from 0.39 (XLM-RoBERTa) to 0.71 (mDeBERTa-v3), a gain of 0.32. Substantial gains are also seen for Amharic (0.52 → 0.73, +0.21), Indonesian (0.57 → 0.71, +0.14), and Swahili (0.65 → 0.75, +0.10). Across all experimental conditions, mDeBERTa-v3 achieves average macro F1 gains ranging from 0.04 to 0.19, with statistical significance (p < 0.02). The model’s advantage is attributed to its disentangled attention mechanism, which enables better generalization across typologically distant languages. These findings establish mDeBERTa-v3 as a novel and more robust architecture for zero-shot cross-lingual hate speech detection, particularly for previously unexplored low-resource languages.
Ghadeer Al-Badani, M. Alsurori, Akram Alsubari· 2026 6th International Confe...· 0 citations
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