Experimental Analysis of Mandarin Tone Pronunciation of Tibetan College Students for Artificial Intelligence Speech Recognition

2021 ◽  
pp. 19-25
Author(s):  
Shiliang Lyu ◽  
Fu Zhang
Keyword(s):  
Artificial Intelligence ◽  
College Students ◽  
Speech Recognition ◽  
Experimental Analysis ◽  
Mandarin Tone

Artificial intelligence speech recognition model for correcting spoken English teaching

2020 ◽  
pp. 1-12
Author(s):  
Duan Ran ◽  
Wang Yingli ◽  
Qin Haoxin
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Basic Knowledge ◽  
Correction Model ◽  
Spoken English ◽  
Speech Recognition Technology ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Important Direction ◽  
And Training

Artificial intelligence speech recognition technology is an important direction in the field of human-computer interaction. The use of speech recognition technology to assist teachers in the correction of spoken English pronunciation in teaching has certain effects and can help students without being constrained by places, time and teachers. Based on artificial intelligence speech recognition technology, this paper improves and analyzes speech recognition algorithms, and uses effective algorithms as the system algorithms of artificial intelligence models. Meanwhile, based on phoneme-level speech error correction, after introducing the basic knowledge, construction and training of acoustic models, the basic process of speech cutting, including the front-end processing of speech and the extraction of feature parameters, is elaborated. In addition, this study designed a control experiment to verify and analyze the artificial intelligence speech recognition correction model. The research results show that the method proposed in this paper has a certain effect.

scholarly journals Validating the Pro-Environmental Behavior Task in a Japanese Sample

2020 ◽  
Vol 12 (22) ◽  
pp. 9534 ◽  
Author(s):  
Florian Lange ◽  
Shimpei Iwasaki
Keyword(s):  
College Students ◽  
Experimental Analysis ◽  
Choice Behavior ◽  
Environmental Behavior ◽  
Cross Cultural ◽  
Environmental Benefits ◽  
Cultural Backgrounds ◽  
Japanese Sample ◽  
Controlled Experimentation ◽  
First Time

Controlled experimentation is critical for understanding the causal determinants of pro-environmental behavior. However, the potential of experimental pro-environmental behavior research is limited by the difficulty to observe pro-environmental behavior under controlled conditions. The Pro-Environmental Behavior Task (PEBT) was developed to address this limitation by facilitating the experimental analysis of pro-environmental behavior in the laboratory. Previous studies in Belgian samples have already supported the validity of the PEBT as a procedure for the study of actual pro-environmental behavior. Here, we aimed for a cross-cultural replication of this finding in a sample of N = 103 Japanese college students. Along the lines of previous studies, we found PEBT choice behavior to be sensitive to within-subject manipulations of its behavioral costs and environmental benefits. This implies that participants take these consequences into account when choosing between PEBT options. In addition, we showed, for the first time, that such consequence effects can also be detected in a less powerful between-subjects design. These results support the generality of consequence effects on PEBT choice behavior as well as the validity and utility of the PEBT for use in samples from different cultural backgrounds.

The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

2020 ◽  
Vol 9 (2) ◽  
pp. 5-9
Author(s):  
Oksana Chulanova
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Decision Support ◽  
Speech Recognition ◽  
Human Resources ◽  
Natural Language ◽  
Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

scholarly journals A combined cepstral distance method for emotional speech recognition

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141771983 ◽  
Author(s):  
Changqin Quan ◽  
Bin Zhang ◽  
Xiao Sun ◽  
Fuji Ren
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Affective Computing ◽  
Recognition Rate ◽  
Support Vector ◽  
Emotional Speech ◽  
Emotion Classification ◽  
Distance Method ◽  
Speech Database ◽  
Emotional Speech Recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%.

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