采用元搜索优化的多特征融合法自动检测嗓音病变

IF 2.5 4区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Erdal Özbay, Feyza Altunbey Özbay, Nima Khodadadi, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
{"title":"采用元搜索优化的多特征融合法自动检测嗓音病变","authors":"Erdal Özbay, Feyza Altunbey Özbay, Nima Khodadadi, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili","doi":"10.1016/j.jvoice.2024.08.018","DOIUrl":null,"url":null,"abstract":"<p><p>Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of 10 common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate, Root-Mean-Square Energy, and Mel-frequency Cepstral Coefficients using a thousand voice signals from the Saarbruecken Voice Database dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors. The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.</p>","PeriodicalId":49954,"journal":{"name":"Journal of Voice","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection.\",\"authors\":\"Erdal Özbay, Feyza Altunbey Özbay, Nima Khodadadi, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili\",\"doi\":\"10.1016/j.jvoice.2024.08.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of 10 common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate, Root-Mean-Square Energy, and Mel-frequency Cepstral Coefficients using a thousand voice signals from the Saarbruecken Voice Database dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors. The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.</p>\",\"PeriodicalId\":49954,\"journal\":{\"name\":\"Journal of Voice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Voice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jvoice.2024.08.018\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Voice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jvoice.2024.08.018","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

声带功能失常等各种因素会导致嗓音病变。基于计算机声学检查的嗓音病变检测对于早期诊断、有效跟踪和改善有问题的语音至关重要。不同的声学测量可提供这种检测。执行这一过程需要专家的监控,由于耗时耗力且成本高昂,并不为患者所青睐。本文旨在检测基于元启发式的自动语音病理学。首先,利用萨尔布吕肯语音数据库数据集中的一千个语音信号,从零交叉率、均方根能量和梅尔频率倒频谱系数中获得了 10 种常见疾病的特征图,包括心肌切除术、发音障碍、前声带外侧部分切除术、接触性咽峡炎、喉炎、白斑病、纯呼气、复发性喉麻痹、声带息肉和老年性声带息肉。为了提高模型的性能,使用这三种方法对从相同疾病的声音中获得的不同特征进行了混合。灰狼优化器(MELGWO)算法基于局部搜索、进化算子和从各种方法中提取的串联特征图,以最大限度地减少特征数量,更快地实现模型,并产生最佳结果。然后使用支持向量机(SVM)和 K 近邻等监督机器学习技术确定元启发式算法的适配值。F1 分数、灵敏度、特异性、准确性和其他评估标准与实验数据进行了比较。使用经改进的 MELGWO 算法优化的特征图的 SVM 分类器取得了 99.50% 的最佳准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection.

Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of 10 common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate, Root-Mean-Square Energy, and Mel-frequency Cepstral Coefficients using a thousand voice signals from the Saarbruecken Voice Database dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors. The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Voice
Journal of Voice 医学-耳鼻喉科学
CiteScore
4.00
自引率
13.60%
发文量
395
审稿时长
59 days
期刊介绍: The Journal of Voice is widely regarded as the world''s premiere journal for voice medicine and research. This peer-reviewed publication is listed in Index Medicus and is indexed by the Institute for Scientific Information. The journal contains articles written by experts throughout the world on all topics in voice sciences, voice medicine and surgery, and speech-language pathologists'' management of voice-related problems. The journal includes clinical articles, clinical research, and laboratory research. Members of the Foundation receive the journal as a benefit of membership.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信