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Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity, and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this article, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and 94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion. [ABSTRACT FROM AUTHOR]

Over the past 10 years, the amount of usergenerated content has increased at phenomenal rates. However, due to a lack of common content descriptors, reliable website searches and access to this content is becoming increasingly difficult. There are third party products available that allow metadata descriptors to be created automatically but the formatting may not be compatible with the website database. With so many different standards for metadata, the whole area is becoming, effectively, de-standardized. This paper will present a cross-standard formatting platform that will allow compatibility between standards. [ABSTRACT FROM PUBLISHER]

Skin color enhancement based on favorite skin color is proposed to make skin color displayed on large screen flat panel TVs agree with human favorite skin color. A robust skin detection method in different intensity is obtained after analyzing the distribution of skin color in HSV color space. The favorite skin color region is found via a psychological experiment too. Then the skin color detected is enhanced with different degrees according to the distance between it and the favorite skin color. The experimental result displayed on 60-inch plasma display panel (PDP) shows that the skin colors detected are approached to the favorite skin color region with different degrees. The skin colors enhanced are more natural and agree with the comprehension of human vision and psychology perceived in advance. [ABSTRACT FROM PUBLISHER]