元音
数学
语音识别
噪音(视频)
平坦度(宇宙学)
发声
频域
统计
听力学
计算机科学
人工智能
医学
物理
图像(数学)
数学分析
量子力学
宇宙学
作者
Vijay Parsa,Donald G. Jamieson
出处
期刊:Journal of Speech Language and Hearing Research
[American Speech–Language–Hearing Association]
日期:2000-04-01
卷期号:43 (2): 469-485
被引量:179
标识
DOI:10.1044/jslhr.4302.469
摘要
We investigated the abilities of four fundamental frequency (F 0 )-dependent and two F 0 -independent measures to quantify vocal noise. Two of the F 0 -dependent measures were computed in the time domain, and two were computed using spectral information from the vowel. The F 0 -independent measures were based on the linear prediction (LP) modeling of vowel samples. Tests using a database of sustained vowel samples, collected from 53 normal and 175 pathological talkers, showed that measures based on the LP model were much superior to the other measures. A classification rate of 96.5% was achieved by a parameter that quantifies the spectral flatness of the unmodeled component of the vowel sample.
科研通智能强力驱动
Strongly Powered by AbleSci AI