TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge

TL;DR

This paper presents a hybrid language identification and multilingual speech recognition system developed for the Interspeech 2025 ML-SUPERB 2.0 Challenge, achieving top overall performance.

Contribution

It introduces a novel hybrid system combining pretrained language embeddings with multiple speech recognition models tailored per language.

Findings

System achieved top overall score in the challenge.

Hybrid approach effectively combines language identification and speech recognition.

Multiple models optimized for different languages based on data availability.

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.