TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge

• URL: http://arxiv.org/abs/2506.01458v1
• Date: Mon, 02 Jun 2025 09:16:09 GMT
• Title: TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge
• Authors: Tanel Alumäe, Artem Fedorchenko,
• Abstract summary: 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.<n>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.<n>The system obtained the top overall score in the challenge.
• Score: 4.297070083645049
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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