Construction of English spoken language system based on machine learning algorithm and natural language recognition

2020 ◽  
Vol 39 (4) ◽  
pp. 4891-4902
Author(s):  
Hongmei Zhu
Keyword(s):  
Speech Recognition ◽  
Natural Language ◽  
Learning Algorithm ◽  
Recognition System ◽  
Ease Of Use ◽  
Speech Recognition System ◽  
Language Recognition ◽  
Language System ◽  
Controlled Natural Language ◽  
Modern Computer

English speech recognition system is affected by a variety of interference factors. Associating the algorithm with the support of modern computer technology can increase the model effect of speech recognition system. Based on the study of the current mainstream controlled natural language thesaurus, this paper proposes a controlled natural language vocabulary classification type. Moreover, this paper defines the domain thesaurus according to the WordNet knowledge description framework, and uses WordNet’s synonym, antisense, upper and lower, etc. In this way, the controlled natural language system can use the semantic relationship of WordNet to identify the words of the non-domain thesaurus input by the user and map the non-domain definition words to the words in the domain thesaurus, thereby improving the ease of use of controlled natural language systems. In addition, this paper designed a controlled experiment to analyze the performance of this system. The research results show that the model constructed in this paper has certain significant effects.

scholarly journals Featurematching by skpcawithunsupervisedalgorithmand maximum probability in speech recognition

2011 ◽  
Vol 1 (1) ◽  
pp. 9-13
Author(s):  
Pavithra M ◽  
Chinnasamy G ◽  
Azha Periasamy
Keyword(s):  
Speech Recognition ◽  
Unsupervised Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Original Data ◽  
Recognition System ◽  
Training Data ◽  
Speech Recognition System ◽  
Maximum Probability ◽  
Great Performance

A Speech recognition system requires a combination of various techniques and algorithms, each of which performs a specific task for achieving the main goal of the system. Speech recognition performance can be enhanced by selecting the proper acoustic model. In this work, the feature extraction and matching is done by SKPCA with Unsupervised learning algorithm and maximum probability. SKPCA reduces the data maximization of the model. It represents a sparse solution for KPCA, because the original data can be reduced considering the weights, i.e., the weights show the vectors which most influence the maximization. Unsupervised learning algorithm is implemented to find the suitable representation of the labels and maximum probability is used to maximize thenormalized acoustic likelihood of the most likely state sequences of training data. The experimental results show the efficiency of SKPCA technique with the proposed approach and maximum probability produce the great performance in the speech recognition system.

scholarly journals An Examination of Emotion Recognition using Machine Learning Algorithms on Different Speech Databases

2020 ◽  
Vol 9 (4S2) ◽  
pp. 37-39
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Natural Language ◽  
Emotion Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Speech Recognition System ◽  
Mel Frequency Cepstral Coefficients ◽  
Natural Languages

The speech recognition system plays a vital role in understanding the emotions of natural language. The identification of emotions from speech is a challenging task. The performance of the speech recognition system is effects on the speech signals. The speech contains different emotions feelings. Many researchers introduced different emotion recognition techniques. However, these techniques achieved better performance but unsatisfied in identify emotion of natural languages. This paper proposed a novel speech recognition system, which identify the emotions based on the speech signals.The Mel Frequency Cepstral Coefficients (MFCC) features. On the resultant features of speech applied crossvalidation using the test emotions. The performance of the proposed system verify with the SVM and other two classifiers. The proposed emotion recognition system achieves better performance. The empirical results shows that the proposed system outperforms when compare with different classifiers and databases.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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