scholarly journals Machine learning methods for sign language recognition: A critical review and analysis

2021 ◽  
Vol 12 ◽  
pp. 200056
Author(s):  
I.A. Adeyanju ◽  
O.O. Bello ◽  
M.A. Adegboye
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Critical Review ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

scholarly journals Estudio de bases de datos para el reconocimiento automático de lenguas de signos

2020 ◽  
Vol 22 ◽  
pp. 145-160
Author(s):  
Darío Tilves Santiago ◽  
Carmén García Mateo ◽  
Soledad Torres Guijarro ◽  
Laura Docío Fernández ◽  
José Luis Alba Castro
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Quality Data ◽  
Complex Task ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
High Quality ◽  
High Quality Data ◽  
Video Information ◽  
Language Technology

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

Arabic Sign Language Recognition Using Deep Machine Learning

2021 ◽  
Author(s):  
Wael Suliman ◽  
Mohamed Deriche ◽  
Hamzah Luqman ◽  
Mohamed Mohandes
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques

2018 ◽  
Author(s):  
Paul D. Rosero-Montalvo ◽  
Pamela Godoy-Trujillo ◽  
Edison Flores-Bosmediano ◽  
Jorge Carrascal-Garcia ◽  
Santiago Otero-Potosi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Machine Learning Techniques ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Learning Techniques
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