Search

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy [ABSTRACT FROM AUTHOR]

Emotion speech recognition is a developing field in machine learning. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. The reliability of the current speech emotion recognition systems is far from being achieved. However, this is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. The speech signals were process with information which is divided into two main categories, linguistic and paralinguistic; emotions belong to the latter tree. The aim of this work is to develop a system that can understand paralinguistic information for paramount better human-machine interactions. A different extracted features like MFCC as well as feature classifications methods like HMM, GMM, LTSTM and ANN were used. In this paper, an improved architecture of CNN for speech emotion recognition were implemented. The main finding that the proposed CNN model achieved 93.96% accuracy rate in detecting emotions. [ABSTRACT FROM AUTHOR]

Human activities recognition from motion capture data is a challenging problem in the computer vision due to the fact that, in various human activities, different body components have distinctive characteristics in terms of the moving pattern. In this paper, a learning method of detecting an activity from different angles based on various sources of information is proposed. with high accuracy. The bottom up approach is used in OpenPose which is the tool used in this paper's experiments. The proposed method achieves promising results on Berkeley Multimodal Human Action Database (MHAD) datasets at 98% accuracy. [ABSTRACT FROM AUTHOR]