Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Speaker recognition is a research topic that is still interesting and challenging. Various problems such as noise problems, poor performance, short duration, spoofing and inconsistency are problems that need to be resolved immediately. The researchers conducted research with various models from traditional methods such as the Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Hidden Markov Model (HMM) to the Deep Learning methods using Deep Neural Network (DNN) and Convolutional Neural Network (CNN). In addition, various hybrid deep learning methods are also used. Various papers that use these methods are difficult to understand, especially when compared between one method with another to obtain novelty and direction of research on speaker recognition. Systematic Literature Review (SLR) is helpful in identifying and interpreting various findings in a field of research in answering the research questions that have determined. This paper uses SLR in identifying research trends,datasets, feature extraction ,classification methods and evaluation techniques used in speaker recognition using deep learning. Results of the SLR discussion are 82 major study journals from 2011 to 2019 show that 20% of research studies focus on speaker verification topics, 11.5% each at Speaker Recognition in Noisy Conditions, Speaker Emotion Recognition and Short and Mismatch Utterance Duration. Research in speaker recognition 90% used public datasets and 10% used private datasets. The MFCC method is a method often used in feature extraction although there are I-vector and X-Vector methods that are starting to be used in deep learning. Deep Neural Network is a classification method that is often used in speaker recognition. 31% of the evaluation techniques that are often used are Equal Error Rate, 29% used the Word Error Rate and 40% used others method such as Accuracy, Root Mean Square Error (RMSE), Signal to Noise Ratio (SNR), Character Error Rate (CER) , Phone Error Rate (PER) and Speech Separation Performance (SSP)
The expansive soil condition will be very complicate for the engineers to make the material mix over it, however it causes a damage risk for the different rigid construction over it due to the soil expanding and shrinking that is very affected by the water content and soil layer. This research intends to investigate the effect of increasing the cement material to the process of soil expanding and shrinking. The laboratory work of index property test is needed for knowing and classifying the soil type that included the CH class based on the AASHTO classification system including the A-7-6. Soil under this class condition is generally very expansive. The measurement of Atterberg limit, Compaction, CBR, and UCS test are used for evaluating the soil properties, This researh make a trial to know the level of Compaction, CBR, UCS of the expansive soil characteristic that is mixed with the different product of cement with the optimal percentage composition. The result shows that the level of expansive soil stabilization is increasing the strength
