Abstract
This paper describes the model built for the SIGTYP 2021 Shared Task aimed at identifying 18 typologically different languages from speech recordings. Mel-frequency cepstral coefficients derived from audio files are transformed into spectrograms, which are then fed into a ResNet-50-based CNN architecture. The final model achieved validation and test accuracies of 0.73 and 0.53, respectively.- Anthology ID:
- 2021.sigtyp-1.13
- Volume:
- Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Ekaterina Vylomova, Elizabeth Salesky, Sabrina Mielke, Gabriella Lapesa, Ritesh Kumar, Harald Hammarström, Ivan Vulić, Anna Korhonen, Roi Reichart, Edoardo Maria Ponti, Ryan Cotterell
- Venue:
- SIGTYP
- SIG:
- SIGTYP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 136–144
- Language:
- URL:
- https://aclanthology.org/2021.sigtyp-1.13
- DOI:
- 10.18653/v1/2021.sigtyp-1.13
- Cite (ACL):
- Giuseppe G. A. Celano. 2021. A ResNet-50-Based Convolutional Neural Network Model for Language ID Identification from Speech Recordings. In Proceedings of the Third Workshop on Computational Typology and Multilingual NLP, pages 136–144, Online. Association for Computational Linguistics.
- Cite (Informal):
- A ResNet-50-Based Convolutional Neural Network Model for Language ID Identification from Speech Recordings (Celano, SIGTYP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.sigtyp-1.13.pdf