{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-modal-transformers-utterance-level-code","title":"Multi-Modal Transformers Utterance-Level Code-Switching Detection","arxiv_id":"2011.02132","date":"2020-11-04","proceeding":null,"authors":["Krishna D N"],"abstract":"An utterance that contains speech from multiple languages is known as a code-switched sentence. In this work, we propose a novel technique to predict whether given audio is mono-lingual or code-switched. We propose a multi-modal learning approach by utilising the phoneme information along with audio features for code-switch detection. Our model consists of a Phoneme Network that processes phoneme sequence and Audio Network(AN), which processes the mfcc features. We fuse representation learned from both the Networks to predict if the utterance is code-switched or not. The Audio Network and Phonetic Network consist of initial convolution, Bi-LSTM, and transformer encoder layers. The transformer encoder layer helps in selecting important and relevant features for better classification by using self-attention. We show that utilising the phoneme sequence of the utterance along with the mfcc features improves the performance of code-switch detection significantly. We train and evaluate our model on Microsoft code-switching challenge datasets for Telugu, Tamil, and Gujarati languages. Our experiments show that the multi-modal learning approach significantly improved accuracy over the uni-modal approaches for Telugu-English, Gujarati-English, and Tamil-English datasets. We also study the system performance using different neural layers and show that the transformers help obtain better performance.","url_abs":"https://arxiv.org/abs/2011.02132v1","url_pdf":"https://arxiv.org/pdf/2011.02132v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-modal-transformers-utterance-level-code","repo_url":"https://github.com/KrishnaDN/Code-Switch-Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}