Effect of alcohol on suprasegmental features of voice

Wakista, G. W.; Abhayarathne, Sachie J.; Mendis, Gihan T.; Arunatileka, Shiromi; Sandaruwan, K. D.; Gunawardena, Sameera A.; Fernando, Ravindra

doi:10.1109/wict.2014.7077277

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The study also found that the individual had a reduced ability to produce specific speech sounds accurately. Wakista et al (2014) [11] researched the effects of an alcoholic beverage on supra-segmental features of voice, which include stress, intonation, and rhythm. For this analysis, they chose 50 males aged between 21 and 50.…”

Section: Literature Reviewmentioning

confidence: 99%

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Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Vital Terlapu,

Prasad Reddy Sadi

2024

IJCDS

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Alcohol consumption can lead to vocal health risks and long-term health issues for individuals. The paper introduces a novel dataset that analyzes vowel vocalizations to detect early alcohol consumption. This study examines hidden parameters in vowel sounds, such as frequency, jitters, shimmer, and harmonic ratio, which can identify individuals who consume alcohol. It aims to identify subtle vocal patterns that serve as markers for alcohol consumption. This study analyzed 509 vowel vocalizations from 290 records of 46 alcohol-consuming individuals and 219 non-drinkers aged 22-34. The study used intelligent machine learning models and Incremental Hidden Layer Neurons Artificial Neural Networks (IHLN-ANNs) with Back-propagation to identify patterns indicative of alcohol consumption. The Random Forest (RF) model achieved 95.3% accuracy, while the BP-ANNs model showed 99.4% accuracy with five neurons in a hidden layer. The findings could be applied to developing smartphone applications to provide timely alerts and cautionary measures for alcohol consumption, reducing accident risks. The study highlights voice analysis's potential as a non-invasive and cost-effective tool for identifying alcohol consumers, offering potential avenues for future public health initiatives.

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Section: Literature Reviewmentioning

confidence: 99%

“…This value is changed in the gradient descent cycle. The overall configuration of one feature neuron is determined using Equation (11).…”

Section: B Dataset Descriptionmentioning

confidence: 99%

Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Vital Terlapu,

Prasad Reddy Sadi

2024

IJCDS

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Drinkers Voice Recognition Intelligent System: An Ensemble Stacking Machine Learning Approach

Terlapu

2024

Ann. Data. Sci.

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Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

Terlapu

2023

Preprint

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Alcohol consumption can have impacts on the voice, and excessive consumption can lead to long-term damage to the vocal cords. A new procedure to automatically detect alcohol drinkers using vowel vocalizations is an earlier and lower-cost method than other alcohol drinker-detecting models and equipment. The hidden parameters of vowel sounds (such as frequency, jitter, shimmer, harmonic ratio, etc.) are significant for recognizing individuals who drink or do not drink. In this research, we analyze 509 multiple vocalizations of the vowels (/a, /e, /i, /o, and /u) from 290 multiple records of 46 drinkers and 219 multiple records of 38 non-drinkers. The age group is 22 to 34 years. Apply the 10-fold cross-validation vowelized dataset on intelligent machine learning models and incremental hidden layer neurons of artificial neural networks (IHLN-ANNs) with backpropagation. The findings showed that experimental ML models such as Naïve Bayes (NB), Random Forest (RF), k-NN, SVM, and C4.5 (Tree) performed well. The RF model performed best, with 95.3% accuracy. We also applied the incremental hidden layer (HL) neurons BP-ANNs model (from 2 to 5). In this analysis, accuracy increased proportionally with the incremental neurons (2–5) in the HL of the ANN. At the moment of 5 neurons HL ANN, the model performed with a highly accurate 99.4% without an over-fit problem. It will implement smartphone apps for caution and alerts for alcohol consumers to avoid accidents. Voice analysis has been explored as a non-invasive and cost-effective means of identifying alcohol consumers.

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Effect of alcohol on suprasegmental features of voice

Cited by 3 publications

References 6 publications

Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Real-time Speech-based Intoxication Detection System: Vowel Biomarker Analysis with Artificial Neural Networks

Drinkers Voice Recognition Intelligent System: An Ensemble Stacking Machine Learning Approach

Intelligent Novel Approach for Identification of Alcohol Consumers using Incremental Hidden Layer Neurons ANN (IHLN-ANN)-Based Model on Vowelized Voice Dataset

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