# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 6 Speech Technology in Industry 4.0
### Speech in Healthcare Applications  
Many studies on the use of voice technology in the healthcare industry have been completed in recent years. The human voice can be used to communicate with devices by doctors, technicians, nurses, and patients. Similarly, emotion recognition-based speech signal analysis could be used in industries to investigate the relationship between worker mood and productivity.  
In [214], an emotion detection model in a smart home environment is proposed. It focuses on children, the elderly, and people who are mentally ill, with the primary goal of identifying pain. In these homes, IoT sensors are distributed to record speech and image signals and send them to the proposed system. The system separates the image and speech signals and makes an emotion decision based on the fused classification score. When the system detects pain in these individuals, it notifies the caregivers. The system claims 99.87 percent accuracy in detecting pain. The proposed system is a healthcare application that is based on 5G data transmission and big data analysis.  
A 5G-based Cognitive telemedicine system [218] is proposed to share high-quality medical resources between a large hospital and rural, remote areas. The solution's goal is to detect people's emotions and, moreover, to diagnose psychological diseases. The system is composed of three layers, where sensors, devices, and network components are included in the infrastructure layer. The task of the resource cognitive layer is resource optimization in order to achieve a green, reliable, flexible, low latency, and scalable solution. The data cognitive engine is built on big data, machine learning, and deep learning. The main goal is to detect the patient's emotional state in order to diagnose psychological diseases. In order to facilitate application establishment, large-scale implementation still needs to address some issues such as privacy, data security, more refined intelligence, and API.  
A lip-reading driven audio-visual hearing-aid is presented in [219], which outperforms audio-only versions in noisy environments but requires significantly more sensor data and processing power. To achieve real-time operation, a 5G IoT network infrastructure with 5 ms latency and 100 Mbps data-rate was required. Audio-visual data must be encrypted on the channel due to privacy concerns. A new light-weight real-time encoding method based on the piece-wise linear chaotic map (PWLCM), chebyshev map, secure hash, and substitution box (S-Box) algorithms is proposed. Speech enhancement is carried out in the cloud. Noisy and visual speech are processed concurrently, and the lip reading model is based on a novel LSTM model, which improves on previous HMM-based models.  
Amazon's Alexa and Google Assistant pave the way for task-specific voice interfaces, and in-home healthcare setup [220]. Voice interfaces act as intermediaries between the user and a variety of medical web services. Patients must use natural language queries rather than navigating through complex/multiple web pages, which may be especially useful for elderly and digitally less savvy users. Patients can access medical information, take medical measurements, communicate with caregivers, make doctor's appointments, and receive proactive medical reminders. A diverse range of services can be added to the pool of services. Table 8 recapitulates the mentioned research regarding the problems, techniques, contributions, and limitations.  
To summarize, enhanced speech-related services are being developed and made available. Some are still in the laboratory or in the testing phase, but they are very close to being ready for market. However, there is a high demand for dependable speech services, so we can anticipate an increase in the number of solutions and end users in the near future.