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.

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 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 Hybrid CTC-Attention Network-Based End-to-End Speech Recognition System for Korean Language

2022 ◽  
Author(s):  
Hosung Park ◽  
Changmin Kim ◽  
Hyunsoo Son ◽  
Soonshin Seo ◽  
Ji-Hwan Kim
Keyword(s):  
Speech Recognition ◽  
Expert Knowledge ◽  
Recognition System ◽  
Training Data ◽  
Experimental Result ◽  
Dramatic Improvement ◽  
Speech Recognition System ◽  
Korean Language ◽  
Attention Network ◽  
End To End

In this study, an automatic end-to-end speech recognition system based on hybrid CTC-attention network for Korean language is proposed. Deep neural network/hidden Markov model (DNN/HMM)-based speech recognition system has driven dramatic improvement in this area. However, it is difficult for non-experts to develop speech recognition for new applications. End-to-end approaches have simplified speech recognition system into a single-network architecture. These approaches can develop speech recognition system that does not require expert knowledge. In this paper, we propose hybrid CTC-attention network as end-to-end speech recognition model for Korean language. This model effectively utilizes a CTC objective function during attention model training. This approach improves the performance in terms of speech recognition accuracy as well as training speed. In most languages, end-to-end speech recognition uses characters as output labels. However, for Korean, character-based end-to-end speech recognition is not an efficient approach because Korean language has 11,172 possible numbers of characters. The number is relatively large compared to other languages. For example, English has 26 characters, and Japanese has 50 characters. To address this problem, we utilize Korean 49 graphemes as output labels. Experimental result shows 10.02% character error rate (CER) when 740 hours of Korean training data are used.

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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