A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

2019 ◽  
Vol 63 (5) ◽  
pp. 50402-1-50402-9 ◽  
Author(s):  
Ing-Jr Ding ◽  
Chong-Min Ruan
Keyword(s):  
Principal Component Analysis ◽  
Automatic Speech Recognition ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Recognition System ◽  
Geometrical Characteristics ◽  
3D Geometry ◽  
Different Types ◽  
The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

scholarly journals THE METHOD OF BASELINE MANIPULATION TO OVERCOME THE SENSOR DRIFT ON GAS SENSOR TEST FOR HERBAL DRINKS DISCRIMINATION

2017 ◽  
Vol 5 (1) ◽  
pp. 55
Author(s):  
Dyah Kurniawati Agustika ◽  
Kuwat Triyana
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Gas Sensor ◽  
Principal Component ◽  
Recognition System ◽  
Sensor System ◽  
Sensor Drift ◽  
Different Types ◽  
Selection Of ◽  
Optimal Type

Abstract Gas sensor system is widely used for the detection of aroma. The main problem in this system is the sensor drift that makes poor reproducibility of the sensor. The reproducibility of the sensor can be improved by applying the feature selection of the sensor’s output response and baseline manipulation. This research focused on determining methods that can reduce the dift sensor of gas sensor by using  basaeline manipulation and selecting the optimal type of baseline manipulation when gas sensor system detects three different types of herbal drinks. The data that have been feature selected were then applied to three different types of baseline manipulation (differential, relative and fractional) and inserted into the pattern recognition system, Principal Component Analysis (PCA). From the analysis of PCA baseline manipulation that gives optimal results is differential one with the value of PC1 82.71%. This shows that differential baseline manipulation is effective in reducing the occurrence of sensor drift. Keywords: electronic nose, gas sensor, baseline manipulation, feature selection

scholarly journals Multilinear principal component analysis for iris biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1458
Author(s):  
Chetana Kamlaskar ◽  
Aditya Abhyankar
Keyword(s):  
Principal Component Analysis ◽  
Iris Recognition ◽  
Feature Fusion ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Good Time ◽  
Wavelet Packet Decomposition ◽  
Multilinear Principal Component Analysis

<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3<sup>rd</sup> order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.</p>

scholarly journals Principal Component Analysis of Munich Functional Developmental Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 227-233
Author(s):  
Grażyna Pazera ◽  
Marta Młodawska ◽  
Jakub Młodawski ◽  
Kamila Klimowska
Keyword(s):  
Principal Component Analysis ◽  
Social Skills ◽  
Motor Development ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Three Dimensions ◽  
Matrix Analysis ◽  
Psychomotor Development ◽  
First Year Of Life

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect developmental deficits. It consists of eight axes on which the following skills are assessed: crawling, sitting, walking, grasping, perception, speaking, speech understanding, social skills. Methods: The study included 110 children in the first year of life examined with the MFDD by the same physician. The score obtained on a given axis was coded as a negative value (defined in months) below the child’s age-specific developmental level. Next, we examined the dimensionality of the scale and the intercorrelation of its axes using polychoric correlation and principal component analysis. Results: Correlation matrix analysis showed high correlation of MFDD axes 1–4, and MFDD 6–8. The PCA identified three principal components consisting of children’s development in the areas of large and small motor skills (axis 1–4), perception (axis 5), active speech, passive speech and social skills (axis 6–8). The three dimensions obtained together account for 80.27% of the total variance. Conclusions: MFDD is a three-dimensional scale that includes motor development, perception, and social skills and speech. There is potential space for reduction in the number of variables in the scale.

scholarly journals Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1321
Author(s):  
Wenjing Quan ◽  
Huiyu Zhou ◽  
Datao Xu ◽  
Shudong Li ◽  
Julien S. Baker ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Sagittal Plane ◽  
Three Dimensional ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Ankle Inversion ◽  
Motion System ◽  
Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

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