构音障碍中的语音压缩 (Utianski et al., 2019)
包含2名构音障碍患者的语音压缩处理样本及20名听者的感知评估数据，用于研究语音压缩对言语可懂度的影响。
基本信息
资源简介
构音障碍中的语音压缩 (Utianski et al., 2019)
【一句话概览】
本数据集是 figshare 公开的多模态数据集，收录 2 个文件、合计 169.97 KB，主题涵盖 speech、compression、algorithm、intelligbility、dysarthria，可用于跨模态对齐与融合建模研究，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Speech compression in dysarthria (Utianski et al., 2019)》。配套论文《The Effects of Speech Compression Algorithms on the Intelligibility of Two Individuals With Dysarthric Speech》发表于 American Journal of Speech-Language Pathology（2019），DOI：10.1044/2018_AJSLP-18-0081。项目资助方：This research was supported in part by National Institute on Deafness and Other Communication Disorders Grants 2R01DC006859 (Liss) and 1R21DC012558 (Liss and Berisha). Rene L. Utianski, Julie M. Liss, and Visar Berisha received research support from the National Institutes of Health.。源站首次发布：2019-02-23。
【数据内容】
源站原始描述：Purpose: Telemedicine, used to offset disparities in access to speech-language therapy, relies on technology that utilizes compression algorithms to transmit signals efficiently. These algorithms have been thoroughly evaluated on healthy speech; however, the effects of compression algorithms on the intelligibility of disordered speech have not been adequately explored. Method: This case study assessed acoustic and perceptual effects of resampling and speech compression (i.e., transcoding) on the…
【数据结构与技术规格】
文件数 2 个，合计 169.97 KB。AJSLP-18-0081utianski_SuppS1.pdf：application/pdf，81.83 KB。AJSLP-18-0081utianski_SuppS2.pdf：application/pdf，88.13 KB。数据集 DOI：10.23641/asha.7291940.v2。
【主题与分类】
源站学科分类：Psycholinguistics (incl. speech production and comprehension)。主题标签：speech、compression、algorithm、intelligbility、dysarthria、dysarthric speech、telemedicine、telepractice、impaired speech、acoustic。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://asha.figshare.com/articles/dataset/Speech_compression_in_dysarthria_Utianski_et_al_2018_/7291940
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Utianski, Rene L.; Sandoval, Steven; Berisha, Visar; Lansford, Kaitlin L.; Liss, Julie M. (2018). Speech compression in dysarthria (Utianski et al., 2019). ASHA journals. Dataset
【使用注意】
· 源站最后修改于 2023-06-01，引用时请注意版本时效。
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Speech compression in dysarthria (Utianski et al., 2019)
- DOI：10.23641/asha.7291940.v2
- 发布日期：2019-02-23
- 文件数：2 个
- 文件总体积：169.97 KB
- 源站页面：https://asha.figshare.com/articles/dataset/Speech_compression_in_dysarthria_Utianski_et_al_2018_/7291940
命令行下载
curl -L -o "AJSLP-18-0081utianski_SuppS1.pdf" "https://ndownloader.figshare.com/files/13696493"
curl -L -o "AJSLP-18-0081utianski_SuppS2.pdf" "https://ndownloader.figshare.com/files/13696490"
文件清单
AJSLP-18-0081utianski_SuppS1.pdf（81.83 KB）AJSLP-18-0081utianski_SuppS2.pdf（88.13 KB）
文件总体积 169.97 KB（源站 API 实测）。
数据说明
Purpose: Telemedicine, used to offset disparities in access to speech-language therapy, relies on technology that utilizes compression algorithms to transmit signals efficiently. These algorithms have been thoroughly evaluated on healthy speech; however, the effects of compression algorithms on the intelligibility of disordered speech have not been adequately explored.
Method: This case study assessed acoustic and perceptual effects of resampling and speech compression (i.e., transcoding) on the speech of 2 individuals with dysarthria. Forced-choice vowel identification and transcription tasks were utilized, completed by 20 naive undergraduate listeners.
Results: Results showed relative improvements and decrements in intelligibility, on various measures, based on the speakers’ acoustic profiles. The transcoding of the speech compression algorithm resulted in an enlarged vowel space area and associated improvements in vowel identification for 1 speaker and a smaller vowel space area and decreased vowel identification for the other speaker. Interestingly, there was an overall decrease in intelligibility in the transcription task in this condition for both speakers.
Conclusions: There is a complex interplay between dysarthria and compression algorithms that warrants further exploration. The findings suggest that it is critical to be mindful of apparent changes in intelligibility secondary to compression algorithms necessary for practicing telemedicine.
Supplemental Material S1. Confusion matrices for each vowel for each condition for Speaker One, where numbers are raw values and indicate the number of tokens identified out of the corpus of 80 classifications per vowel; target vowels are indicated with phonetic (International Phonetic Alphabet [IPA]) notation; corresponding perceived vowels are indicated with orthographic notation.
Supplemental Material S2. Confusion matrices for each vowel for each condition for Speaker Two, where numbers are raw values and indicate the number of tokens identified out of the corpus of 80 classifications per vowel; target vowels are indicated with phonetic (International Phonetic Alphabet [IPA]) notation; corresponding perceived
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.23641/asha.7291940.v2（source: https://asha.figshare.com/articles/dataset/Speech_compression_in_dysarthria_Utianski_et_al_2018_/7291940）。
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