ML-SUPERB 2.0: Multilingual Speech Benchmark

• ML-SUPERB 2.0 Challenge is a comprehensive benchmark that evaluates self-supervised and supervised speech models across 142 languages and 15 datasets.
• It applies strict parameter constraints and innovative adaptation strategies, including LoRA and adapters, to ensure fair and robust model comparisons.
• The challenge emphasizes real-world performance with metrics for fairness and efficiency, guiding research in low-resource and diverse acoustic environments.

The ML-SUPERB 2.0 Challenge is an extensive multilingual benchmark designed to objectively evaluate speech processing models—especially self-supervised and supervised foundation models—across diverse languages, datasets, adaptation regimes, and modeling constraints. Building on the lineage of SUPERB and ML-SUPERB, this iteration introduces rigorous methodological extensions, new adaptation protocols, and enhanced fairness metrics, shaping the evaluation landscape for universal speech technologies.

1. Benchmarking Framework and Methodology

ML-SUPERB 2.0 employs a comprehensive evaluation protocol modeled to reflect real-world conditions for multilingual ASR (Automatic Speech Recognition) and LID (Language Identification). The evaluation suite spans 142 languages and 15 datasets, using a 1-hour training and 10-minute dev/test split per language-dataset pairing, resulting in a corpus of ≈300 hours. A distinctive few-shot scenario is included, assigning only five training utterances to 20 reserved languages to probe data efficiency and generalization.

All model submissions must satisfy a strict parameter constraint: no more than 100 million tunable parameters per configuration. This constraint enables fair comparison—especially when massive foundation models are included via frozen representations.

The primary metrics are macro-averaged Character Error Rate (CER) for ASR and accuracy for LID, with additional focus on:

• Standard deviation (SD) of CER across languages, highlighting robustness,
• CER on the worst-performing language (WL),
• Cross-dataset CER range for languages appearing in multiple corpora,
• LID accuracy,
• Performance in few-shot (ultra-low resource) settings.

Model adaptation and downstream architectures are systematically varied. Configurations include:

• Shallow two-layer downstream models (as in previous ML-SUPERB benchmarks),
• Larger architectures such as Transformer, Conformer, and E-Branchformer, used in both CTC and CTC-attention hybrid discriminative frameworks.

2. Model Adaptation Approaches and Practical Strategies

ML-SUPERB 2.0 systematically explores adaptation strategies for both SSL and supervised models. The approaches include:

• Frozen encoder with shallow heads: The backbone remains fixed, only the small downstream model is trained.
• Full fine-tuning: All model layers are updated, achieving the best results in the standard setting but requiring more resources.
• Partial fine-tuning: Only specific model layers (often middle layers) are updated, exploiting representational stratification within deep models. Empirically, middle-layer partial tuning outperforms both top-layer and bottom-layer fine-tuning.
• Adapters: Small trainable networks (e.g., per-layer bottleneck adapters, dimension 64) are inserted into the main model and only those are updated.
• LoRA (Low-Rank Adaptation): Low-rank matrices are added to certain attention layers, updated during adaptation while the main parameters are kept frozen. For example, LoRA rank 16 with scaling α = 16.

Results show LoRA slightly outperforms adapters for equivalent parameter budgets and can approach full fine-tuning performance in standard settings, providing dramatic gains over frozen baselines. The empirical finding is that LoRA and adapters offer strong trade-offs for efficient adaptation, especially valuable for rapid deployment or low-resource scenarios.

Data augmentation is employed to compensate for few-shot limitations, with supplementing utterances from external sources greatly improving downstream LID and ASR performance in ultra-low-resource languages.

3. Impact of Downstream Architecture on Performance

The downstream model design crucially influences both absolute performance and the comparative ranking of backbone models. E-Branchformer architectures consistently outperform both standard Transformers and Conformers, achieving lower CER and improved LID accuracy (e.g., CER: 16.6% for MMS + E-Branchformer vs 24.8% for XLS-R + baseline Transformer under original ML-SUPERB settings). Hybrid CTC-attention frameworks typically outperform pure CTC in full-data regimes, while pure CTC retains an edge in few-shot scenarios.

The parameter constraint (≤100M) ensures the comparability and accessibility of submissions, but further highlights architectural scaling limits: increasing model size confers diminishing returns in low-data regimes, favoring more parameter-efficient architectures and adaptation methods.

4. Language and Dataset Robustness

A key insight of ML-SUPERB 2.0 is the pronounced variability in ASR performance across languages and datasets:

• Standard deviations of CER across languages range from 10% to as high as 22%, underlining large gaps in model robustness.
• The worst-performing languages (e.g., Lao, Min Nan Chinese) routinely exhibit CERs more than twice the overall mean.
• Within-language, cross-dataset variance is also substantial. For example, Urdu receives a CER of 21.8% on one dataset (Common Voice) but 56.9% on another (Fleurs), reflecting major domain and acoustic biases.

These findings emphasize a genuine need for targeted adaptation methods and fairness-aware evaluation metrics, as universal models remain highly sensitive to language typology, training resource coverage, and dataset/domain characteristics.

5. Challenge Results, Baseline Progress, and Techniques

Recent results from ML-SUPERB 2.0 and its associated Interspeech 2025 challenge demonstrate significant advances:

• The winning TalTech system uses a hybrid LID approach (combining deep language embeddings from a frozen SeamlessM4T encoder with a phonotactic, bigram LM-driven ASR rerank system), modular ASR model selection (finetuned SeamlessM4T, MMS-1B-all with language adapters, MMS-zeroshot) per language, and achieves 86.8% LID accuracy and 27.4% CER (vs. baselines of 53.8%/51.9%)(2506.01458). Language adapters and custom fine-tuning are employed for languages with poor baseline coverage.
• The runner-up (Xiaomi et al.) (2505.24200) employs data augmentation for few-shot languages, partial fine-tuning (especially of middle encoder layers), and auxiliary LID CTC regularization, attaining 14% relative LID accuracy and 30% CER improvement over frozen baselines.
• Model adaptation techniques and data supplementing strategies—both supervised and self-supervised—are empirically validated. LoRA and partial layer tuning are consistently strong, especially when combined with targeted data augmentation and auxiliary regularization.
• No single adaptation or modeling approach dominates across all settings; robust performance depends on synergizing model selection, resource-aware adaptation, and aggressive augmentation.

6. Practical Considerations, Limitations, and Future Directions

ML-SUPERB 2.0 is explicitly designed to drive methodological rigor and real-world impact:

• The inclusion of robustness/fairness metrics shifts focus from mean performance to outlier and least-served cases, explicitly addressing the risk of systemic underperformance on low-resource, dialectal, or out-of-domain data.
• The challenge exposes the limitations of simply scaling model size, confirming that intelligent adaptation, architectural choices, and domain-matched training are required for equitable generalization.
• Efficient adaptation protocols (adapters, LoRA) are not only critical for real-world system deployment but are also essential for scalable evaluation and transfer learning in resource-constrained environments.
• Persistent challenges remain, including handling extreme low-resource languages, robustly bridging cross-domain gaps, and addressing the pronounced acoustic and linguistic diversity present in global speech data.

Open research avenues include:

• Systematic development of language-aware adaptation and learning strategies,
• Improved fairness metrics and mitigation techniques,
• Stronger benchmarks for data and model documentation, supply chain transparency, and reproducibility, especially as demanded by large-scale model sharing and foundation model evaluation.

7. Summary Table: ML-SUPERB 2.0 Key Technical Dimensions

Aspect	Features/Requirements	Key Metric(s)
Languages	142+ (>15 datasets, 300 h total)	Macro CER, LID acc., SD, WL
Downstream models	Shallow baseline, Transformer/Conformer/E-Branchformer (CTC/CTC-ATT)	CER, LID, param. count
Model adaptation	Frozen, partial/full fine-tune, Adapters, LoRA	CER/LID vs. adaptation type
Parameter constraint	≤100M tunable	-
Robustness/fairness	Macro SD, worst-CER, cross-set range	SD, WL, few-shot LID/CER
Efficiency	Evaluation of adaptation cost, methods for low-resource deployment	-

ML-SUPERB 2.0 establishes a new benchmark paradigm for the evaluation of multilingual speech models—setting explicit standards for comprehensive, equitable, and efficient performance across a global spectrum of languages and real-world domains.