Recognition accuracy with a voice-recognition system designed for anesthesia record keeping

The present study examined three factors and their interactions which make us feel a voice-recognition system is inconvenient as a device for sending information: sending unit, accuracy of recognition per unit, and response time per unit. The main results were as follows: (1) All three factors influenced feelings of the inconvenience of a voice recognition system. (2) Sending tasks even with the smallest unit such as monosyllable do not cause feelings of inconvenience when recognition accuracy is high (95% or 100%) and response occurs almost in real time. (3) Recognition accuracy and response time cannot compensate for each other to reduce feelings of inconvenience.

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OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.8947 ◽

2020 ◽

Vol 5 (2) ◽

pp. 504

Author(s):

Matthias Omotayo Oladele ◽

Temilola Morufat Adepoju ◽

Olaide ` Abiodun Olatoke ◽

Oluwaseun Adewale Ojo

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Word Recognition ◽

Support Vector Machine Classifier ◽

Recognition Accuracy ◽

Recognition System ◽

Support Vector ◽

Geometric Features ◽

Total Length ◽

Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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Faculty Opinions recommendation of Failure to recognize loss of incoming data in an anesthesia record-keeping system may have increased medical liability.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14245.471743 ◽

2006 ◽

Author(s):

Frederick Cheney

Keyword(s):

Medical Liability ◽

Anesthesia Record ◽

Record Keeping

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ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

Recent Patents on Computer Science ◽

10.2174/2213275912666190819113541 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 1

Author(s):

Basavaraj N Hiremath ◽

Malini M Patilb

Keyword(s):

Feature Extraction ◽

Computer Program ◽

Principle Component Analysis ◽

Voice Recognition ◽

Component Analysis ◽

Recognition System ◽

Principle Component ◽

Whole Process ◽

The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

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A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware

Sensors ◽

10.3390/s21010222 ◽

2020 ◽

Vol 21 (1) ◽

pp. 222

Author(s):

Tao Li ◽

Chenqi Shi ◽

Peihao Li ◽

Pengpeng Chen

Keyword(s):

Frequency Domain ◽

Gesture Recognition ◽

Channel State Information ◽

Cross Correlation ◽

Recognition Accuracy ◽

Correlation Method ◽

Bottom Layer ◽

Recognition System ◽

Channel State ◽

State Information

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

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Voice recognition system having word frequency and intermediate result display features

The Journal of the Acoustical Society of America ◽

10.1121/1.411231 ◽

1994 ◽

Vol 96 (5) ◽

pp. 3211-3211

Author(s):

Shinta Kimura

Keyword(s):

Word Frequency ◽

Voice Recognition ◽

Recognition System ◽

Intermediate Result

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Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Sensors ◽

10.3390/s21020524 ◽

2021 ◽

Vol 21 (2) ◽

pp. 524

Author(s):

Kyoung Jun Noh ◽

Jiho Choi ◽

Jin Seong Hong ◽

Kang Ryoung Park

Keyword(s):

Domain Adaptation ◽

Recognition Accuracy ◽

Recognition System ◽

Heterogeneous Databases ◽

Second Best ◽

Finger Vein ◽

Adversarial Network ◽

Image Characteristics ◽

Vein Recognition ◽

Finger Vein Recognition

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

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