Word spotting in a speech recognition system without predetermined endpoint detection

1993 ◽  
Vol 93 (3) ◽  
pp. 1677-1677
Author(s):  
Ira A. Gerson
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Endpoint Detection ◽  
Word Spotting

scholarly journals Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study

10.2196/18677 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e18677
Author(s):  
Weifeng Fu
Keyword(s):  
Mental Health ◽  
Speech Recognition ◽  
Psychological Treatment ◽  
Recognition System ◽  
Training Data ◽  
Mental Health Consultation ◽  
Speech Recognition System ◽  
Parallel Operation ◽  
Endpoint Detection ◽  
Health Counseling

Background Speech recognition is a technology that enables machines to understand human language. Objective In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling. Methods A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education. Results The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition. Conclusions The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system.

scholarly journals Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Weifeng Fu
Keyword(s):  
Mental Health ◽  
Speech Recognition ◽  
Psychological Treatment ◽  
Recognition System ◽  
Training Data ◽  
Mental Health Consultation ◽  
Speech Recognition System ◽  
Parallel Operation ◽  
Endpoint Detection ◽  
Health Counseling

BACKGROUND Speech recognition is a technology that enables machines to understand human language. OBJECTIVE In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling. METHODS A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education. RESULTS The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition. CONCLUSIONS The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system.

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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