# TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge

arXiv: https://arxiv.org/abs/2506.01458 · HTML: https://mlsanity.com/paper/2506.01458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [GlotLID: Language Identification for Low-Resource Languages](https://mlsanity.com/paper/2310.16248/) (2023)
- [Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox'ochitl Mixtec](https://mlsanity.com/paper/2101.10877/) (2021)
- [Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation](https://mlsanity.com/paper/2204.02470/) (2022)
- [Improving Language Identification of Accented Speech](https://mlsanity.com/paper/2203.16972/) (2022)
- [Scaling A Simple Approach to Zero-Shot Speech Recognition](https://mlsanity.com/paper/2407.17852/) (2024)

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

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

## Abstract (arXiv)

This paper describes the language identification and multilingual speech recognition system developed at Tallinn University of Technology for the Interspeech 2025 ML-SUPERB 2.0 Challenge. A hybrid language identification system is used, consisting of a pretrained language embedding model and a light-weight speech recognition model with a shared encoder across languages and language-specific bigram language models. For speech recognition, three models are used, where only a single model is applied for each language, depending on the training data availability and performance on held-out data. The model set consists of a finetuned version of SeamlessM4T, MMS-1B-all with custom language adapters and MMS-zeroshot. The system obtained the top overall score in the challenge.