scholarly journals Tongue‐Body Motion during Selected Speech Sounds

1968 ◽  
Vol 44 (1) ◽  
pp. 354-354 ◽  
Author(s):  
Robert A. Houde
Keyword(s):  
Body Motion ◽  
Speech Sounds

Editorial Note

1946 ◽  
Vol 11 (1) ◽  
pp. 2-2
Keyword(s):  
Editorial Note ◽  
Speech Sounds ◽  
Left Hand ◽  
Hand Column ◽  
Right Hand ◽  
Infant Speech ◽  
The Right

In the article “Infant Speech Sounds and Intelligence” by Orvis C. Irwin and Han Piao Chen, in the December 1945 issue of the Journal, the paragraph which begins at the bottom of the left hand column on page 295 should have been placed immediately below the first paragraph at the top of the right hand column on page 296. To the authors we express our sincere apologies.

Distributional Learning in Categorization of Speech and Non-Speech Sounds

2012 ◽  
Author(s):  
Megan M. Kittleson ◽  
Jessamyn Schertz ◽  
Randy Diehl ◽  
Andrew J. Lotto
Keyword(s):  
Speech Sounds ◽  
Distributional Learning

Study on the Differences in the Results of Body Shape Test According to the Position of the Two Feet and the Usefulness of the Neck and Body Motion Image Test

2020 ◽  
Vol 9 (1) ◽  
pp. 22-26
Author(s):  
Wan Song Chang ◽  
◽  
Song Ja Kim ◽  
Seo Won Ryu ◽  
Duk Joon Lim ◽  
...  
Keyword(s):  
Body Shape ◽  
Body Motion ◽  
Image Test

Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

2020 ◽  
Vol 48 (4) ◽  
pp. 287-314
Author(s):  
Yan Wang ◽  
Zhe Liu ◽  
Michael Kaliske ◽  
Yintao Wei
Keyword(s):  
Elastic Deformation ◽  
Estimation Algorithm ◽  
Force Sensor ◽  
Euler Method ◽  
Rolling Contact ◽  
Body Motion ◽  
Identification Algorithm ◽  
Active Perception ◽  
Kinematics Model ◽  
Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Speech Sound ◽  
The Elderly ◽  
Speech Sounds ◽  
Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

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