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The Consensus Auditory-Perceptual Evaluation of Voice is a widely employedtool in clinical voice quality assessment that is significant for streamingcommunication among clinical professionals and benchmarking for thedetermination of further treatment. Currently, because the assessment relies onexperienced clinicians, it tends to be inconsistent, and thus, difficult tostandardize. To address this problem, we propose to leverage lightly weightedautomatic audio parameter extraction, to increase the clinical relevance,reduce the complexity, and enhance the interpretability of voice qualityassessment. The proposed method utilizes age, sex, and five audio parameters:jitter, absolute jitter, shimmer, harmonic-to-noise ratio (HNR), and zerocrossing. A classical machine learning approach is employed. The result revealsthat our approach performs similar to state-of-the-art (SOTA) methods, andoutperforms the latent representation obtained by using popular audiopre-trained models. This approach provide insights into the feasibility ofdifferent feature extraction approaches for voice evaluation. Audio parameterssuch as jitter and the HNR are proven to be suitable for characterizing voicequality attributes, such as roughness and strain. Conversely, pre-trainedmodels exhibit limitations in effectively addressing noise-related scorings.This study contributes toward more comprehensive and precise voice qualityevaluations, achieved by a comprehensively exploring diverse assessmentmethodologies.

Lightly Weighted Automatic Audio Parameter Extraction for the Quality Assessment of Consensus Auditory-Perceptual Evaluation of Voice