口吃声学分析：机器学习研究数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Acoustic analysis in stuttering: a machine-learning study.docx
• DOI：10.3389/fneur.2023.1169707.s001
• 发布日期：2023-06-30
• 文件数：1 个
• 文件总体积：22.55 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Acoustic_analysis_in_stuttering_a_machine-learning_study_docx/23606154

命令行下载

curl -L -o "Data_Sheet_1_Acoustic analysis in stuttering: a machine-learning study.docx" "https://ndownloader.figshare.com/files/41410482"

文件清单

• Data_Sheet_1_Acoustic analysis in stuttering: a machine-learning study.docx（22.55 KB）

文件总体积 22.55 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundStuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).
ObjectiveWe assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine – SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.
MethodsFifty-three PWS (20 children, 33 younger adults) and 71 age−/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN).
ResultsAcoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings.
ConclusionAcoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its rela

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2023.1169707.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Acoustic_analysis_in_stuttering_a_machine-learning_study_docx/23606154）。