A challenge systems paper reporting the top-scoring ML-SUPERB 2.0 entry, built from a hybrid language identifier and a per-language selection of three pretrained ASR models.
TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge
A challenge systems paper reporting the top-scoring ML-SUPERB 2.0 entry, built from a hybrid language identifier and a per-language selection of three pretrained ASR models.