Research of Speech Recognition Based on Neural Network

2014 ◽  
Vol 539 ◽  
pp. 136-140
Author(s):  
Jing Zhai Zhang ◽  
Xiang Dong Qiao ◽  
Peng Zhou Zhang
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Information Science ◽  
Identification Problem ◽  
Recognition System ◽  
Brain Science ◽  
The Neural Network ◽  
Intelligence Studies ◽  
Modeling Theory ◽  
Mathematics And Physics

As a newly cross subject which began in the 1940 s, the neural network plays an important part in human intelligencehuman intelligencehuman intelligence studies, has been a attention and research hotspot in many subjects such as information science, brain science, psychology, mathematics and physics. The neural network has well aabstract categoriesabstract categoriesaaa bstract categories capability, which has been applied to the research and development of speech recognition system, and become an effective tool for resolving the identification problem. This paper mainly analyzes the philosophy and procedures of speech recognition, and modeling theory and characteristics of the neural network, discusses the application of neural network in speech recognition.

scholarly journals COMPARISON OF OPTIMIZATION ALGORITHMS OF CONNECTIONIST TEMPORAL CLASSIFIER FOR SPEECH RECOGNITION SYSTEM

2019 ◽  
Vol 9 (3) ◽  
pp. 54-57
Author(s):  
Yedilkhan Amirgaliyev ◽  
Kuanyshbay Kuanyshbay ◽  
Aisultan Shoiynbek
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Optimization Algorithms ◽  
Recognition System ◽  
Model Data ◽  
Short Term ◽  
Term Memory ◽  
The Neural Network ◽  
Long Short Term Memory

This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long-Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Sign in / Sign up

Export Citation Format

Share Document