Normalized Vowel Duration Enhanced RNN Prosodic Phrase Detection Model

Author(s):  
Yizhi Wu ◽  
Hongyan Li ◽  
Sha Li
1995 ◽  
Vol 38 (5) ◽  
pp. 1014-1024 ◽  
Author(s):  
Robert L. Whitehead ◽  
Nicholas Schiavetti ◽  
Brenda H. Whitehead ◽  
Dale Evan Metz

The purpose of this investigation was twofold: (a) to determine if there are changes in specific temporal characteristics of speech that occur during simultaneous communication, and (b) to determine if known temporal rules of spoken English are disrupted during simultaneous communication. Ten speakers uttered sentences consisting of a carrier phrase and experimental CVC words under conditions of: (a) speech, (b) speech combined with signed English, and (c) speech combined with signed English for every word except the CVC word that was fingerspelled. The temporal features investigated included: (a) sentence duration, (b) experimental CVC word duration, (c) vowel duration in experimental CVC words, (d) pause duration before and after experimental CVC words, and (e) consonantal effects on vowel duration. Results indicated that for all durational measures, the speech/sign/fingerspelling condition was longest, followed by the speech/sign condition, with the speech condition being shortest. It was also found that for all three speaking conditions, vowels were longer in duration when preceding voiced consonants than vowels preceding their voiceless cognates, and that a low vowel was longer in duration than a high vowel. These findings indicate that speakers consistently reduced their rate of speech when using simultaneous communication, but did not violate these specific temporal rules of English important for consonant and vowel perception.


2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.


Author(s):  
Julio Acedo ◽  
Marcos Fernandez-Sellers ◽  
Adolfo Lozano-Tello
Keyword(s):  

2020 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
ZHANG Rui-yan ◽  
◽  
JIANG Xiu-jie ◽  
AN Jun-she ◽  
CUI Tian-shu ◽  
...  

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