Speech-based system for detecting alcohol intoxication using optimized deep learning.
In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks...
| Publicado en: | Psychopharmacology pp. 1 - 15 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193360504&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193360504 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00333158 EJD jtl: Psychopharmacology issn: 00333158 maglogo: N pubinfo: dt: May2026 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193360504 10.1007/s00213-026-07065-0 193360504 ppf: 1 ppct: 14 formats: tig: atl: Speech-based system for detecting alcohol intoxication using optimized deep learning. aug: au: Abirami, S. Vasudevan, V. Dhanasekaran, S. affil: Department of Computer Applications, Kalasalingam Academy of Research and Education sug: ab: In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings.In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings.In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings.In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings.In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings.Graphical abstract: In this research, we propose a highly accurate speech-based system for detecting alcohol intoxication, utilizing deep learning algorithms to classify speech as either sober or intoxicated. The core of this system is the Dense Residual Recurrent Network (D2R_Net), which combines dense residual blocks for spatial feature extraction with Gated Recurrent Units (GRUs) for modeling temporal dependencies in speech data. This architecture is further optimized using the Iterative Parrot Optimization (ItPaO) algorithm, a novel hyperparameter tuning method designed to enhance the model’s convergence and classification accuracy. The speech data is first pre-processed into log-Mel spectrograms, which allow the model to capture key auditory patterns altered by alcohol consumption. Through extensive experimentation, the proposed system outperformed existing models, with a balanced accuracy of 98.51% and specificity reaching 98.2%. The optimized model is highly efficient, requiring minimal computational resources while providing robust and scalable performance, making it ideal for deployment in real-world scenarios. This non-invasive, cost-effective alcohol detection system is poised to revolutionize public safety applications, such as monitoring drivers, event security, and healthcare settings.Introduces a non-invasive deep learning model for alcohol intoxication detection using speech signals.Utilizes Dense Residual Recurrent Network (D2R_Net) for effective spatial and temporal feature extraction.Enhances model performance with the Iterative Parrot Optimization (ItPaO) algorithm.Achieves 98.51% balanced accuracy and 98.2% specificity in detecting intoxicated speech.Offers an efficient, scalable solution for alcohol screening in public safety settings. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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