Efficient Affective State Identification in Vocal Signals through Machine Learning-Based Neural Frameworks

Efficient identification of affective states from vocal signals remains a critical challenge in biomedical signal processing and computational intelligence due to the inherent variability, noise sensitivity, and non-stationary characteristics of speech data. This study proposes a machine learning-based neural framework that integrates wavelet packet decomposition, adaptive feature optimization, and hybrid classification models for robust emotional and pathological voice state recognition. The system leverages multiresolution analysis to extract discriminative acoustic features while employing optimization-driven feature selection strategies inspired by evolutionary computation and neural adaptation principles.

The proposed framework is grounded in signal decomposition techniques such as lifting wavelet transforms and wavelet packet representations, which enable efficient localization of temporal and spectral speech characteristics. Feature engineering is further enhanced through statistical descriptors including Mel-frequency cepstral coefficients, jitter measures, and complexity-based acoustic parameters. These features are subsequently processed using machine learning classifiers such as support vector machines, linear discriminant analysis, and hybrid neural architectures to achieve high classification accuracy.

The methodology is informed by prior research on pathological voice classification and adaptive signal processing, where wavelet-based feature extraction and genetic algorithm optimization have demonstrated strong performance in distinguishing subtle variations in vocal patterns (Ariased-Londono et al.; Saidi & Almasganj). Additionally, the study incorporates biologically inspired computational principles that align with neural adaptation mechanisms described in computational neuroscience literature (Doya, 1999), enhancing interpretability and adaptive learning capacity.

Experimental design considerations highlight robustness across noisy and heterogeneous speech datasets, particularly using benchmark corpora such as the Disordered Voice Database. The proposed framework emphasizes computational efficiency while maintaining high sensitivity in affective state classification tasks.

The findings indicate that hybrid machine learning models combining wavelet-based feature extraction with neural optimization significantly outperform conventional statistical classifiers in terms of accuracy, robustness, and generalization capability. The study contributes to advancing affective computing systems by providing a scalable and interpretable framework for vocal emotion and pathology detection, with applications in healthcare diagnostics, human–computer interaction, and intelligent assistive systems (Anoop et al., 2018).

Affective computing, voice signal processing, wavelet packet transform, support vector machines

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