# 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 cancellation is implemented and tested using traditional signal processing and deep learning. Speech recognition word error rares are used to assess efficiency. The proposed active noise cancellation algorithm provides a measurable advantage in the \(-10\) to \(-30\) dB SNR range. In general, the measured noise reduction is \(-10\) dB, but at resonance frequencies, up to \(-25\) dB is achieved. Table 10 overviews speech with industrial noise issues about the techniques and contributions. Google Home is a popular smart speaker system that processes speech in the cloud. When using Google Home, the acoustics are far from ideal, as the user is usually far away from the device, and a significant amount of environmental noise is recorded as well. As a result, efficient acoustic modeling is critical for Google Home applications [241]. The Google Home includes a two-microphone array. Two channels of speech can be processed, allowing for improved speech quality while also necessitating more computational power. To jointly process multichannel audio data, the processing workflow includes a weighted prediction error algorithm for dereverberation, a factored Complex Linear Projection, and a Grid LSTM. To cover real-life scenarios, the 18k h of training data includes several room, noise \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Problems** & **Techniques** & **Contribution** & **Ref.** \\ \hline Speech enhancement in noisy environment & Acoustic and adversarial supervision (AAS) algorithm & AAS gained lower word error rate than traditional methods & [234] \\ \hline Speech enhancement in electric vehicles & Improved nonnegative matrix factorization (ImNMF) & Decrease the effect of noise on human–machine interaction & [235] \\ \hline Bearing fault diagnosis in industrial environment & Convolutional neural networks and spectral kurtosis filtering & High classification accuracy even in noise & [236] \\ \hline Speech enhancement with a wearable microphone array & The time of audio arrival measurement & Significantly higher recognition accuracy in noisy environment & [237] \\ \hline Noise cancellation implemented on cochlear implant devices & Deep learning-based noise classifier and deep denoising autoencoder & High intelligibility score, fully integrated into cochlear implant signal processors & [238] \\ \hline Voice activity detection in noise & Adaptive context attention model & Computationally improved speech–noise separation & [239] \\ \hline Active noise cancellation with microphone arrays & Two sets of MEMS resonant microphone arrays; deep learning-based noise cancellation & Effective noise cancellation for automatic speech recognition tasks & [240] \\ \hline Google Home acoustic model enhancement & Microphone array, weighted prediction error algorithm, factored Complex Linear Projection and Grid LSTM & [241] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of Speech with Industrial Noise Issues. type, noise level, and spekaer position simulations. The evaluation was carried out on clean, simulated, and real data, yielding a relative word error rate reduction of 8-28 percent when compared to the existing production system.