TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912409417940992 |
|---|---|
| author | Alumäe, Tanel Fedorchenko, Artem |
| author_facet | Alumäe, Tanel Fedorchenko, Artem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge Alumäe, Tanel Fedorchenko, Artem Computation and Language Audio and Speech Processing 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. |
| title | TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge |
| topic | Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.01458 |