# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 6 Speech Technology in Industry 4.0
### Noisy Speech in Industrial Environments  
Noise is regarded as one of the most significant barriers to using speech in industrial settings. Many efforts have been made to reduce background noise in speech and to provide robust speech processing. Acoustic and Adversarial Supervision (AAS) is a speech enhancement algorithm proposed in [234] to improve noisy speech recognition while preserving clean speech recognition. To train, both clean and noisy data are used, but unlike traditional methods, clean and noisy audio samples do not need to be paired. It was put through its paces on two noisy datasets: CHiME-4 and Librispeech + DEMAND. According to the paper, AAS outperformed other speech enhancement methods in terms of word error rate. High accuracy human machine interfaces are required in self-driving cars, but mechanical and electric power equipment noises in electric vehicles can be significant, and they are quite different from traditional vehicles. Therefore, ref. [235] introduced a new method for reducing speech noise for human-machine interaction in electric vehicles. This method for improving speech is based on improved nonnegative matrix factorization (ImNMF), which outperforms traditional nonnegative matrix factorization methods. The analysis of special signals, such as the sound of a bearing, is critical in industrial scenarios. Finding a faulty bearing in good time can help you avoid costly breakdowns. A noisy environment, such as a factory, engine, or environmental noise, complicates the mission of bearing fault diagnosis. Convolutional Neural Networks (CNNs) were used in [236] to classify bearing faults, and spectral kurtosis filtering was used to reduce the noise in the raw data. The method was tested on the CWRU bearing dataset, and the improvement is detectable at \(-10\) dB SNR. A wearable MEMS microphone array was presented in [237] to improve speech in a noisy environment. The project's goal is to improve speech recognition accuracy while focusing on motor-impaired people for whom voice is a convenient or, in some cases, the only way to control a computer. The time of audio arrival between the microphones in the array was measured to separate speech from environmental noise. In good SNR situations, the microphone array performs similarly to the single microphone version, but significantly better when SNR is less than 20 dB. The Cochlear Implant (CI) is an implantable electronic device that replaces the function of potentially damaged inner ear parts. Implementing advanced signal processing in CI devices to enable higher speech intelligibility is a difficult task. To improve speech intelligibility, Ref. [238] implemented a novel deep learning-based noise classifier and deep denoising auto-encoder method. Clinical listening tests were conducted on Mandarin-speaking cochlear implant recipients to evaluate the method. The intelligibility scores outperformed all of the other methods tested. Voice activity detection methods are used to separate speech from noise. It is used in most speech-related applications, for example, to distinguish speech from background noise or to save processing power. Because signal classification models can be trained on known environmental noises, speech-signal context is important for some voice activity detection methods. Unpredictable noisy environments are investigated in [239]. The adaptive context attention model makes use of context information by implementing a neural network architecture. The results are compared to those of other methods. Deep neural network models performed similarly, but the proposed model only had half or a third of the parameters of the others. An active noise cancellation setup is described in [240]. A pair of MEMS resonant microphone arrays are used, each with a different resonance frequency. One array covers the frequency range 0.8-5 kHz, while the other covers the frequency range 5-9 kHz. The lower range is used for speech recording, while the upper range is used for noise cancellation.