SPELL: A Pronunciation Training Device Based on Speech Technology

1994 ◽  
pp. 90-97
Author(s):  
J.-P. Lefèvre

1975 ◽  
Author(s):  
Robert R. Mackie
Keyword(s):  


1984 ◽  
Author(s):  
Paul Roller Michaelis
Keyword(s):  


1990 ◽  
Author(s):  
ARMY TRAINING SUPPORT CENTER FORT EUSTIS VA


2020 ◽  
Vol 54 (5) ◽  
pp. 23-28
Author(s):  
E.V. Fomina ◽  
◽  
T.B. Kukoba ◽  

Testing of 25 cosmonauts showed that the amount of resistance training weight loading in long-term space mission influences dynamics of the leg-muscle strength and velocity recovery. On Earth, the loads equal from 70 to 130 % of the body mass is sufficient for keeping up endurance and maximum strength moments of shin and thigh muscles. In the group of cosmonauts who had not used the strength training device or chosen loads less than 30 % of the body mass the leg-muscle maximum strength and thigh endurance were decreased substantially on day 4 of return and all the more by day 15 back on Earth.



Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 341
Author(s):  
Carolina Rodriguez-Paras ◽  
Johnathan T. McKenzie ◽  
Pasakorn Choterungruengkorn ◽  
Thomas K. Ferris

Despite the increasing availability of technologies that provide access to aviation weather information in the cockpit, weather remains a prominent contributor to general aviation (GA) accidents. Pilots fail to detect the presence of new weather information, misinterpret it, or otherwise fail to act appropriately on it. When cognitive demands imposed by concurrent flight tasks are high, the risks increase for each of these failure modes. Previous research shows how introducing vibrotactile cues can help ease or redistribute some of these demands, but there is untapped potential in exploring how vibratory cues can facilitate “interruption management”, i.e., fitting the processing of available weather information into flight task workflow. In the current study, GA pilots flew a mountainous terrain scenario in a flight training device while receiving, processing, and acting on various weather information messages that were displayed visually, in graphical and text formats, on an experimental weather display. Half of the participants additionally received vibrotactile cues via a connected smartwatch with patterns that conveyed the “severity” of the message, allowing pilots to make informed decisions about when to fully attend to and process the message. Results indicate that weather messages were acknowledged more often and faster when accompanied by the vibrotactile cues, but the time after acknowledgment to fully process the messages was not significantly affected by vibrotactile cuing, nor was overall situation awareness. These findings illustrate that severity-encoded vibrotactile cues can support pilot awareness of updated weather as well as task management in processing weather messages while managing concurrent flight demands.



2021 ◽  
pp. 1-11
Author(s):  
J. N. de Boer ◽  
A. E. Voppel ◽  
S. G. Brederoo ◽  
H. G. Schnack ◽  
K. P. Truong ◽  
...  

Abstract Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.



Author(s):  
Guoqing Wan ◽  
Hsieh-Chun Hsieh ◽  
Chien-Heng Lin ◽  
Hung-Yu Lina ◽  
Chien-Yu Linb ◽  
...  


2015 ◽  
Vol 197 ◽  
pp. 371-377
Author(s):  
Mohamed Essaoudi ◽  
Raja Lotfi ◽  
Said Lotfi ◽  
Mohammed Talbi ◽  
Mohamed Radid




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