# Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC

arXiv: https://arxiv.org/abs/2505.24200 · HTML: https://mlsanity.com/paper/2505.24200/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 32 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition](https://mlsanity.com/paper/2210.00077/) (2022)
- [OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer](https://mlsanity.com/paper/2401.16658/) (2024)
- [ML-SUPERB: Multilingual Speech Universal PERformance Benchmark](https://mlsanity.com/paper/2305.10615/) (2023)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions](https://mlsanity.com/paper/2104.02724/) (2021)
- [Towards Robust Speech Representation Learning for Thousands of Languages](https://mlsanity.com/paper/2407.00837/) (2024)
- [OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification](https://mlsanity.com/paper/2402.12654/) (2024)
- [Improving Massively Multilingual ASR With Auxiliary CTC Objectives](https://mlsanity.com/paper/2302.12829/) (2023)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [A Configurable Multilingual Model is All You Need to Recognize All Languages](https://mlsanity.com/paper/2107.05876/) (2021)
- [ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets](https://mlsanity.com/paper/2406.08641/) (2024)
- [ASR2K: Speech Recognition for Around 2000 Languages without Audio](https://mlsanity.com/paper/2209.02842/) (2022)
- [Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks](https://mlsanity.com/paper/1910.10324/) (2019)
- [Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning](https://mlsanity.com/paper/2309.15317/) (2023)
- [SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition](https://mlsanity.com/paper/2309.16937/) (2023)

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

- [Geolocation-Aware Robust Spoken Language Identification](https://mlsanity.com/paper/2508.17148/) (2025)

## Abstract (arXiv)

Multilingual speech processing with self-supervised or supervised pre-trained Speech Foundation Models (SFM) has achieved strong performance on tasks like Language Identification (LID) and Automatic Speech Recognition (ASR). However, these models struggle with limited resources during fine-tuning. This paper enhances multilingual LID and ASR on ML-SUPERB 2.0 by exploring multiple strategies for adapting SFMs, including frozen upstream training, partial fine-tuning, and low-rank adaptation. Furthermore, we employ data augmentation to mitigate performance gaps in few-shot settings and introduce LID Connectionist Temporal Classification (CTC) loss for regularization. Our approach achieves a 14% relative improvement in LID accuracy and a 30% relative reduction in ASR CER over the baseline on ML-SUPERB 2.0, securing second place in the Interspeech 2025 ML-SUPERB 2.0 Challenge.