Bruno Rodrigues, Hugo Cordeiro, Gonçalo C. Marques
{"title":"语音信号中喉病理鉴别的频谱能量带:健康与不健康声音鉴别、病理鉴别","authors":"Bruno Rodrigues, Hugo Cordeiro, Gonçalo C. Marques","doi":"10.23919/CISTI58278.2023.10212052","DOIUrl":null,"url":null,"abstract":"This work presents a model to discriminate between healthy and unhealthy voices with physiological laryngeal pathologies, between healthy and unhealthy voices with neuromuscular laryngeal pathologies, and between pathological voices with both types of mentioned pathologies. The model is based on the analysis of speech signal energy in different frequency bands, specifically in its mean value and variation over the signal. The accuracy rates obtained were 100% when discriminating between healthy and unhealthy voices with physiological laryngeal pathologies, 96.55% when discriminating between healthy and unhealthy voices with neuromuscular pathologies, and 93.48% when discriminating between physiological and neuromuscular laryngeal pathologies. The results demonstrate that certain frequency bands contain the information needed for the three discrimination processes performed.","PeriodicalId":121747,"journal":{"name":"2023 18th Iberian Conference on Information Systems and Technologies (CISTI)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spectral energy bands for laryngeal pathologies discrimination in speech signals : Healthy and unhealthy voices discrimination, and pathology discrimination\",\"authors\":\"Bruno Rodrigues, Hugo Cordeiro, Gonçalo C. Marques\",\"doi\":\"10.23919/CISTI58278.2023.10212052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a model to discriminate between healthy and unhealthy voices with physiological laryngeal pathologies, between healthy and unhealthy voices with neuromuscular laryngeal pathologies, and between pathological voices with both types of mentioned pathologies. The model is based on the analysis of speech signal energy in different frequency bands, specifically in its mean value and variation over the signal. The accuracy rates obtained were 100% when discriminating between healthy and unhealthy voices with physiological laryngeal pathologies, 96.55% when discriminating between healthy and unhealthy voices with neuromuscular pathologies, and 93.48% when discriminating between physiological and neuromuscular laryngeal pathologies. The results demonstrate that certain frequency bands contain the information needed for the three discrimination processes performed.\",\"PeriodicalId\":121747,\"journal\":{\"name\":\"2023 18th Iberian Conference on Information Systems and Technologies (CISTI)\",\"volume\":\"237 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 18th Iberian Conference on Information Systems and Technologies (CISTI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CISTI58278.2023.10212052\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 18th Iberian Conference on Information Systems and Technologies (CISTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CISTI58278.2023.10212052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spectral energy bands for laryngeal pathologies discrimination in speech signals : Healthy and unhealthy voices discrimination, and pathology discrimination
This work presents a model to discriminate between healthy and unhealthy voices with physiological laryngeal pathologies, between healthy and unhealthy voices with neuromuscular laryngeal pathologies, and between pathological voices with both types of mentioned pathologies. The model is based on the analysis of speech signal energy in different frequency bands, specifically in its mean value and variation over the signal. The accuracy rates obtained were 100% when discriminating between healthy and unhealthy voices with physiological laryngeal pathologies, 96.55% when discriminating between healthy and unhealthy voices with neuromuscular pathologies, and 93.48% when discriminating between physiological and neuromuscular laryngeal pathologies. The results demonstrate that certain frequency bands contain the information needed for the three discrimination processes performed.