Research on Speech Quality Evaluation Method Based on Deep Neural Network

2019 ◽  
Author(s):  
Teng Haikun ◽  
Wang Shiying ◽  
Li Lunbin
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Speech Quality

A Novel Recurrent Generalized Congruence Neural Network Measure for Objective Speech Quality Evaluation

2012 ◽  
Vol 433-440 ◽  
pp. 2282-2287
Author(s):  
Tian Yun Yan
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Speech Quality ◽  
System Model ◽  
The Novel ◽  
Training Time ◽  
Network Measure ◽  
Comparison Results ◽  
Objective Speech Quality ◽  
New System

A new system model for objective speech quality evaluation based on the improved recurrent generalized congruence neural network (RGCNN/OSQE) is proposed. The performance of the RGCNN model is compared with the most commonly used RBFNN (radial basis function neural network) model in objective speech quality evaluation. Comparison results show that the RGCNN model has higher correlation coefficient, less deviation, and saves about half training time, i.e., the RGCNN model has obvious advantages over the RBFNN model. Therefore, the novel RGCNN model for objective speech quality evaluation is feasible and effective.

scholarly journals A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Cheng ◽  
Yingying Cai ◽  
Haomai Chen ◽  
Zhuang Cai ◽  
Gang Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Learning Strategies ◽  
Deep Neural Network ◽  
Evaluation Method ◽  
Bloom's Taxonomy ◽  
Automatic Evaluation ◽  
Cognitive Level ◽  
Text Data ◽  
Bloom’S Taxonomy

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

scholarly journals Sonar image quality evaluation using deep neural network

2021 ◽  
Author(s):  
Huiqing Zhang ◽  
Shuo Li ◽  
Donghao Li ◽  
Zichen Wang ◽  
Qixiang Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Image Quality Evaluation ◽  
Sonar Image

Objective Speech Quality Evaluation Based on Matlab

2014 ◽  
Vol 568-570 ◽  
pp. 284-287
Author(s):  
Ji Xiang Wang ◽  
Shu Li Dong
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Objective Evaluation ◽  
Wavelet Spectrum ◽  
Speech Quality ◽  
Simulation Environment ◽  
Matlab Simulation ◽  
Distortion Measure ◽  
Simulation Results ◽  
Objective Speech Quality

The speech quality is one of the most important indexes to evaluate the communication system. The objective evaluation is the last goal that people have been pursuing. The most efficient evaluation methods based on the spectrum distortion measure was analysed. The objective evaluation method based on wavelet spectrum distortion measure was simulated on the Matlab simulation environment. The simulation results show that Matlab was very suit to analyse the speech quality. The objective evaluation rules are found out on Matlab and the objective evaluation method was achieved.

scholarly journals A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

2021 ◽  
Author(s):  
Rohun Nisa
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Deep Neural Network ◽  
Speech Quality ◽  
Hybrid Network ◽  
Speech Communication ◽  
Mean Square ◽  
Perceptual Evaluation ◽  
Speech Spectrum ◽  
Fully Connected

In the speech communication process, the desirable speech needs to be addressed under the influence of noise encountered in diverse environments that degrade the speech quality and intelligibility. In opposition to the unfavorable scenario particularly lowered signal-to-noiseratio, the progress of traditional noise suppressive algorithms is hindered, introducing further distortion in speech, making them non-applicable for real-time applications. In order to reduce the complicacies of current algorithms, a hybrid approach for upgrading the quality together with intelligibility of speech is proposed for dealing with real-world hearing scenario. For improving the intelligibility of speech of interest, multiple sub-frame analysis using over-spectral subtractive factor with phase recompense approach is implemented on the multi-channel noise corrupted speech, yielding approximated speech spectrum, that constitutes the pre-processing stage. The approximated speech spectrum and clean speech spectrum forming the training set are further fed to Fully Connected Layered Deep Neural Network to reduce the mean square error with the incorporation of regression network resulting in improved quality for speech. The proposed hybrid network results in upgraded intelligibility and quality in speech signal with improved SNR measured in terms of Short-Time-Objective-Intelligibility (STOI) score, Perceptual-Evaluation-of-Speech-Quality (PESQ) score, Segmental SNR level, and Mean Square Error (MSE) in contrast to prior noise suppressive algorithms together with less complexity of the hybrid network.<br>

scholarly journals Japanese Translation Quality Evaluation System Based on Deep Neural Network Algorithm

2022 ◽  
Vol 16 ◽  
pp. 433-439
Author(s):  
Rui Zhang
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Processing Efficiency ◽  
Translation Quality ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quality Evaluation System ◽  
Japanese Translation

The current translation quality evaluation system relies on the combination of manual and text comparison for evaluation, which has the defects of low efficiency and large evaluation errors. In order to optimize the defects of the current quality evaluation system, a Japanese translation quality evaluation system based on deep neural network algorithm will be designed. In order to improve the processing efficiency of the system, the USB3.0 communication module of the hardware system will be optimized. Based on the hardware design, the reference translation map is used to extend the reference translation of Japanese translation. The evaluation indexes of over- and under-translation are set, and the evaluation of Japanese translation quality is realized after the parameters are determined by training the deep neural network using the sample set. The system functional test results show that the average data transmission processing time of the system is improved by about 31.27%, and the evaluation error interval is smaller and the evaluation is more reliable.

scholarly journals Developing a new mesh quality evaluation method based on convolutional neural network

2020 ◽  
Vol 14 (1) ◽  
pp. 391-400 ◽  
Author(s):  
Xinhai Chen ◽  
Jie Liu ◽  
Yufei Pang ◽  
Jie Chen ◽  
Lihua Chi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Mesh Quality

Evaluation of English interpretation teaching quality based on GA optimized RBF neural network

2020 ◽  
pp. 1-8
Author(s):  
Lu Chen ◽  
He Being
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Principal Component ◽  
Teaching Quality ◽  
Network Teaching ◽  
Time Performance

Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.

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