A Recent paper on using Voice Recognition and machine learning to detect drowsiness when driving.
Jasim, S. S., Abdul Hassan , A. K. and Turner , S. (2022) “Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10(2), pp. 142-151. doi: 10.14500/aro.11000.
Full text at: https://aro.koyauniversity.org/index.php/aro/article/view/1000
Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively.
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