Voice of any human plays an important role in communication and sharing information among each other. Through voice, internal behavior can be identified as to whether the person is happy or angry which is reflected. A person’s behavior is not exactly reflected by their face; variation in his/her voice reflects somehow their behavior as there will be variation in voice and variation in frequency and pitch. Feelings and natural behaviors are important features, and there are many biological aspects through which they can be identified. Therefore, in this paper, we consider a Hindi speech specimen of different groups to identify the person’s behavior and natural feelings under different acoustic conditions. Many research papers show emotion recognition based on neural networks with different models using speech signals to identify the present status of any patient, and it helps to build a way for a smart healthcare system. Enabling service in terms of Blockchain means the sufferer does not require communicating with complex and failed tasks for collecting information from various sources to send to their expert. Blockchain provides experts access to systems and enables entry to the dataset section. Patients have total control over their data, and they no longer require monitoring to keep their data managed and up to date. Also, manually coordinating with data is required for multiple visitors, which can be a very tedious one. In this paper, we focused on feature removal of speech using different extraction approaches which were used to know the quality or state of voice specimens and also understand which feature extraction plays a vital role in gaining a close state of speech. Internet of Things-based learning platforms are used to gather the voice sample, and also, a deep gaining method was followed to reach and achieve the best accuracy and identify the error rate which will help to gather close behavior and state of mind. Finally, a proposed model based on the Gaussian mixture model as a classifier was used for its spotting and attestation.

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.

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Speech is one of the major communication tools to share information among people. This exchange method has a complicated construction consisting of not the best imparting of voice but additionally consisting of the transmission of many-speaker unique information. The most important aim of this research is to extract individual features through the speech-dependent health monitoring and management system; through this system, the speech data can be collected from a remote location and can be accessed. The experimental analysis shows that the proposed model has a good efficiency. Consequently, in the last 5 years, many researchers from this domain come in front to explore various aspects of speech which includes speech analysis using mechanical signs, human system interaction, speaker, and speech identification. Speech is a biometric that combines physiological and behavioural characteristics. Especially beneficial for remote attack transactions over telecommunication networks, the medical information of each person is quite a challenge, e.g., like COVID-19 where the medical team has to identify each person in a particular region that how many people got affected by some disease and took a quick measure to get protected from such diseases and what are the safety measure required. Presently, this task is the most challenging one for researchers. Therefore, speech-based mechanisms might be useful for tracking his/her voice quality or throat getting affected. By collecting the database of people matched and comparing with his/her original database, it can be identified in such scenarios. This provides the better management system without touching and maintains a safe distance data that can be gathered and processed for further medical treatment. Many research studies have been done but speech-dependent approach is quite less and it requires more work to provide such a smart system in society, and it may be possible to reduce the chances to come into contact with viral effected people in the future and protect society for the same.

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