Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples.
Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples.
复制标题
来自语音声学和发音样本中ALS的个体的可理解说话速度自动预测。
DOI：
10.1080/17549507.2018.1508499
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发表时间：
2018-11
影响因子：
1.8
通讯作者：
Green JR
中科院分区：
文献类型：
--
作者：
Wang J;Kothalkar PV;Kim M;Bandini A;Cao B;Yunusova Y;Campbell TF;Heitzman D;Green JR
This research aimed to automatically predict intelligible speaking rate for individuals with Amyotrophic Lateral Sclerosis (ALS) based on speech acoustic and articulatory samples. Twelve participants with ALS and two normal subjects produced a total of 1,831 phrases. NDI Wave system was used to collect tongue and lip movement and acoustic data synchronously. A machine learning algorithm (i.e. support vector machine) was used to predict intelligible speaking rate (speech intelligibility × speaking rate) from acoustic and articulatory features of the recorded samples. Acoustic, lip movement, and tongue movement information separately, yielded a R2 of 0.652, 0.660, and 0.678 and a Root Mean Squared Error (RMSE) of 41.096, 41.166, and 39.855 words per minute (WPM) between the predicted and actual values, respectively. Combining acoustic, lip and tongue information we obtained the highest R2 (0.712) and the lowest RMSE (37.562 WPM). The results revealed that our proposed analyses predicted the intelligible speaking rate of the participant with reasonably high accuracy by extracting the acoustic and/or articulatory features from one short speech sample. With further development, the analyses may be well-suited for clinical applications that require automatic speech severity prediction.
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DOI：
10.1109/tnsre.2017.2681691
发表时间：
2017-09
期刊：
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Kim M;Kim Y;Yoo J;Wang J;Kim H
通讯作者：
Kim H
影响因子：
2.8
作者：
Rong P;Yunusova Y;Wang J;Green JR
通讯作者：
Green JR
影响因子：
4.4
作者：
Cedarbaum, JM;Stambler, N;Nakanishi, A
通讯作者：
Nakanishi, A
DOI：
10.1080/21678421.2017.1303515
发表时间：
2017-01-01
影响因子：
2.8
作者：
Allison, Kristen M.;Yunusova, Yana;Green, Jordan R.
通讯作者：
Green, Jordan R.
影响因子：
3.2
作者：
Cummins, Nicholas;Scherer, Stefan;Quatieri, Thomas F.
通讯作者：
Quatieri, Thomas F.