Abstract
This study considers an approach to the analysis of human speech using neural networks to perform the task of determining the state of alcahol intoxication. In the course of the study, a personal data set was created, consisting of 340 audio files, and increased to the size of 1020 audio files, using augmentation methods, namely slowing down and speeding up audio files. As input data for neural networks, spectrograms and MFCC visualization with size of 256 × 256 and 512 × 512 pixels were considered, thanks to which four VGG16 models were trained. As a result of the study, the best model was identified, namely, trained on spectrograms of size 512 × 512, having F-score and UAR values of 0.73 and 0.77 for the test samples, and 0.82 and 0.83 in the form of averages of 10-fold cross-validation.
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Acknowledgments
This research was funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of scientific projects carried out by teams of research laboratories of educational institutions of higher education subordinate to the Ministry of Science and Higher Education of the Russian Federation, project number FEWM-2020–0042. The authors would like to thank the Irkutsk Supercomputer Center of SB RAS for providing access to the HPC-cluster «Akademik V.M. Matrosov» [24].
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Laptev, P., Litovkin, S., Kostyuchenko, E. (2023). Determining Alcohol Intoxication Based on Speech and Neural Networks. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_9
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