ESTIMATION OF HABIT-RELATED INFORMATION FROM MALE VOICE DATA USING MACHINE LEARNING-BASED METHODS
Abstract:
The analysis of human behavior and habits has gained significant attention in various fields, including psychology, healthcare, and marketing. In recent years, advancements in machine learning techniques have opened up new possibilities for extracting valuable information from large-scale datasets. This abstract focuses on the estimation of habit-related information from male voice data through the application of machine learning-based methods.
Voice data contains a wealth of information, including acoustic features, prosodic patterns, and speech characteristics that can reveal insights into an individual’s habits. Previous studies have demonstrated that voice analysis can provide valuable information about smoking, alcohol consumption, stress levels, and other habit-related factors.
This research aims to develop a robust machine learning framework capable of accurately estimating habit-related information from male voice data. The proposed methodology involves the following steps: data collection, preprocessing, feature extraction, and model training. A diverse dataset of male voice recordings will be collected, including individuals with different habits and lifestyle choices.
The preprocessing step will involve noise reduction, speech segmentation, and voice normalization to ensure data quality and consistency. Feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs), pitch, and formant analysis will be employed to capture relevant acoustic and linguistic characteristics from the voice data.
Various machine learning algorithms, including support vector machines (SVM), random forests, and deep learning models, will be trained and evaluated to estimate habit-related information accurately. The models will be trained on a labeled dataset, where habit-related information is obtained through self-reporting or external sources.
The performance of the developed models will be evaluated using appropriate metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques will be employed to assess the generalization capability of the models.
The findings of this research have the potential to contribute to the development of personalized habit monitoring systems, healthcare interventions, and targeted marketing campaigns. By accurately estimating habit-related information from male voice data, it will be possible to gain insights into individuals’ habits in a non-intrusive and cost-effective manner.
Keywords: Habit-related information, Voice data analysis, Machine learning, Feature extraction, Model training, Personalized habit monitoring.