{"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/speaker-fluency-level-classification-using","title":"Speaker Fluency Level Classification Using Machine Learning Techniques","arxiv_id":"1808.10556","date":"2018-08-31","proceeding":null,"authors":["Alan Preciado-Grijalva","Ramon F. Brena"],"abstract":"Level assessment for foreign language students is necessary for putting them\nin the right level group, furthermore, interviewing students is a very\ntime-consuming task, so we propose to automate the evaluation of speaker\nfluency level by implementing machine learning techniques. This work presents\nan audio processing system capable of classifying the level of fluency of\nnon-native English speakers using five different machine learning models. As a\nfirst step, we have built our own dataset, which consists of labeled audio\nconversations in English between people ranging in different fluency\ndomains/classes (low, intermediate, high). We segment the audio conversations\ninto 5s non-overlapped audio clips to perform feature extraction on them. We\nstart by extracting Mel cepstral coefficients from the audios, selecting 20\ncoefficients is an appropriate quantity for our data. We thereafter extracted\nzero-crossing rate, root mean square energy and spectral flux features, proving\nthat this improves model performance. Out of a total of 1424 audio segments,\nwith 70% training data and 30% test data, one of our trained models (support\nvector machine) achieved a classification accuracy of 94.39%, whereas the other\nfour models passed an 89% classification accuracy threshold.","url_abs":"http://arxiv.org/abs/1808.10556v1","url_pdf":"http://arxiv.org/pdf/1808.10556v1.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":"speaker-fluency-level-classification-using","repo_url":"https://github.com/agrija9/Avalinguo-Dataset-Speaker-Fluency-Level-Classification-Paper-","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"speaker-fluency-level-classification-using","repo_url":"https://github.com/geetha2601/Speaker-fluency","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}