# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 6 Speech Technology in Industry 4.0
### Speech with Security Issues  
Speaker identification and speech recognition are becoming increasingly important in secure applications such as electronic payments. We must be aware of information security issues and work to mitigate potential threats in this area. In many cases, the human-machine interaction interface of smart IoT devices is natural speech. E-health, smart building control, and Apple Siri are examples of IoT applications that use speech input control (see Figure 16). According to [228], privacy is a serious issue in the used speech services because the audio data and recognition results are transferred unencrypted, making personal data vulnerable. To protect privacy in IoT scenarios, a long short term memory (LSTM) neural network-based secret sharing protocol is introduced.  
The accuracy of speech recognition using Kinect sensors may be inefficient and uncertain due to the sensor's random placement [229]. A client-server speech recognition system was proposed to overcome the drawbacks of the traditional Kinect-SDK speech recognition method. Multiple Kinect sensors are linked via TCP/IP, and sensor selection is handled by a decision server. Several decision-making and sensor-placing strategies were investigated. Using multiple Kinect sensors resulted in a 28% relative improvement, while processing time increased by only 13%.  
In the 5G network, the number of devices that share information on social media will be enormous. The growing volume of multimedia content necessitates the development of data integrity solutions. In [230], a Speech Watermarking technique is introduced to improve the integrity of sharing speech or audio signals in the 5G wireless network. The Least-Significant-Bit substitution technique is used in this method, which employs the Filter Bank Multicarrier Modulation (FBMC) Technique. The embedded watermark outperformed existing techniques in terms of resistance to various attacks.  
Inaudible voice attacks are a threat to speech recognition systems used in smart homes. The use of ultrasound carriers to modulate human voice produces inaudible signals for humans, but speech recognition devices may detect and respond to this. Watchdog is a signal processing-based detection method described in [231]. Environmental signals are being recorded using an ultrasonic microphone. A low level alert is issued whenever a suspicious frequency is detected. The ultrasonic signal is demodulated, and a voice activity detector is used to determine the presence of human voice in the demodulated signal, resulting in a high level alert. To avoid false alarms, ambient noise must be analyzed.  
Because of overlapping speech, it is difficult to identify speakers in crowded places such as meetings or debates. Ref. [232] proposed two methods for overlapping automatic speaker identification (OSID) with up to five concurrent speakers. In the first stage, the two-stage method determines the number of simultaneous speakers and then identifies them in the signal. The single-stage method achieves OSID directly in one step by employing a single classifier. Both OSID systems use one-dimensional convolutional neural networks, which outperform multilayer perceptron and Gaussian mixture model-based systems. In a  
Figure 16: Smart IoT devices controlled by speech commands.  
clean dataset, the two-stage system with five speakers has an accuracy of 98.55 percent, while the one-stage system has 95.07 percent.  
Speaker recognition in [233], such as the previous one, is based on one-dimensional convolutional neural networks. The method was developed using the Xilinx VC709 platform. The model reduces the computational complexity of ResNet20 by 64% and the number of parameters by 53%, allowing for efficient FPGA implementation. Furthermore, the model can handle speech data of varying lengths.