Speech Recognition Using Enhanced Features with Deep Belief Network for Real Time Application

2021 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Deep Belief Network ◽  
Belief Network ◽  
Real Time Application

scholarly journals Speech Recognition of Oral English Teaching Based on Deep Belief Network

2020 ◽  
Vol 15 (10) ◽  
pp. 100
Author(s):  
Jianmei Wang
Keyword(s):  
Speech Recognition ◽  
Language Learning ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Evaluation Model ◽  
Speech Rate ◽  
Pearson Correlation ◽  
Deep Belief Network ◽  
Belief Network ◽  
English Teaching

The oral English teaching faces several common problems: the teaching method is very inefficient, and the learners are poor in oral English. The development of computer-aided language learning offers a possible solution to these problems. Based on techniques of speech recognition, cloud computing and deep learning, this paper applies the deep belief network (DBN) to recognize the speeches in oral English teaching, and establishes a multi-parameter evaluation model for the pronunciation quality of oral English among college students. The model combines the merits of subjective and objective evaluations, and assesses the pronunciation from four aspects: pitch, speech rate, rhythm and intonation. Finally, the proposed model was verified through speech recognition and pronunciation evaluation experiments on 26 non-English majors from a college. The results show that the proposed evaluation model output credible results, which are consistent with those of experts, as evidenced by consistency, neighbourhood consistency and Pearson correlation coefficient. The research provides a feasible way to evaluate the oral English proficiency of learners, laying the basis for improving the teaching and learning efficiency of oral English.

A real-time and ubiquitous network attack detection based on deep belief network and support vector machine

2020 ◽  
Vol 7 (3) ◽  
pp. 790-799 ◽  
Author(s):  
Hao Zhang ◽  
Yongdan Li ◽  
Zhihan Lv ◽  
Arun Kumar Sangaiah ◽  
Tao Huang
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Attack Detection ◽  
Deep Belief Network ◽  
Support Vector ◽  
Network Attack ◽  
Belief Network ◽  
Ubiquitous Network

scholarly journals Real-time classification and sensor fusion with a spiking deep belief network

2013 ◽  
Vol 7 ◽  
Author(s):  
Peter O'Connor ◽  
Daniel Neil ◽  
Shih-Chii Liu ◽  
Tobi Delbruck ◽  
Michael Pfeiffer
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Deep Belief Network ◽  
Belief Network ◽  
Real Time Classification

scholarly journals A Real-time Application Framework for Web-Based Speech Recognition Using HTTP/2 and SSE

2018 ◽  
Vol 12 (3) ◽  
pp. 1230
Author(s):  
Kalamullah Ramli ◽  
Asril Jarin ◽  
Suryadi Suryadi
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Full Duplex ◽  
Application Framework ◽  
Web Based ◽  
Speech Data ◽  
Real Time Application

The performance of network-based speech recognition application is mainly determined by the availability of all speech data received on the server and also the realtimeness in delivering the recognition results from the server. On the basis of full-duplex speech recognition application this paper proposes a real-time application framework for web speech recognition using HTTP/2 protocol and Sender-Sent Events (SSE). A number of experiments were performed to compare the latency of both the application using HTTP/2 plus SSE and the full-duplex application using WebSocket. The results showed that the proposed framework offers better alternative for a web-based speech recognition than the framework using WebSocket.

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