Recognition Rate Prediction for Dysarthric Speech Disorder Via Speech Consistency Score

2006 ◽  
pp. 885-889
Author(s):  
Prakasith Kayasith ◽  
Thanaruk Theeramunkong ◽  
Nuttakorn Thubthong
Keyword(s):  
Recognition Rate ◽  
Speech Disorder ◽  
Rate Prediction ◽  
Consistency Score ◽  
Dysarthric Speech

scholarly journals Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning

2019 ◽  
Vol 16 (10) ◽  
pp. 1688 ◽  
Author(s):  
Zhichao Li ◽  
Jilin Huang ◽  
Zhiping Hu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Speech Disorder ◽  
Diagnostic Methods ◽  
Common Disease ◽  
Long Duration ◽  
Wide Range ◽  
Rapid Diagnostic Method ◽  
Voice Data

Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and avoid misdiagnosis, the search for an affordable and rapid diagnostic method is becoming more and more important for chronic pharyngitis research. Speech disorder is one of the typical symptoms of patients with chronic pharyngitis. This paper introduces a convolutional neural network model for diagnosis based on the typical symptom of speech disorder. First of all, the voice data is converted into a speech spectrogram, which can better output the speech characteristic information and lay a foundation for computer diagnosis and discrimination. Second, we construct a deep convolutional neural network for the diagnosis of chronic pharyngitis through the design of the structure, the design of the network layer, and the description of the function. Finally, we perform a parameter optimization experiment on the convolutional neural network and judge the recognition efficiency of chronic pharyngitis. The results show that the convolutional neural network has a high recognition rate for patients with chronic pharyngitis and has a good diagnostic effect.

Testing the Significance of the Agreement Among Observers

1968 ◽  
Vol 11 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Martin A. Young ◽  
Tom D. Downs
Keyword(s):  
Rating Scale ◽  
Intraclass Correlation ◽  
Speech Disorder ◽  
Interquartile Range ◽  
Speech Pathology ◽  
Observer Ratings

Ratings by observers are often used in speech pathology to measure complex speech dimensions; this seems reasonable since a speech “disorder” represents the product of an observer’s evaluation and a speaker’s performance. An index of the validity of these evaluations may be estimated by the amount of agreement among the observers. In this paper, the semi-interquartile range and the intraclass correlation are discussed as possible indices of agreement, and another index is suggested, based on the range of observer ratings. Under the assumption that the distribution of ratings is uniform when ratings are randomly assigned, that is, the observers show no agreement, tables were constructed to indicate the probability of any range for selected numbers of observers and rating scale categories. Some applications for this index concern the training of observers, estimating the number of observers needed, and the construction of master scales.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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