  The Consensus Auditory-Perceptual Evaluation of Voice is a widely employed
tool in clinical voice quality assessment that is significant for streaming
communication among clinical professionals and benchmarking for the
determination of further treatment. Currently, because the assessment relies on
experienced clinicians, it tends to be inconsistent, and thus, difficult to
standardize. To address this problem, we propose to leverage lightly weighted
automatic audio parameter extraction, to increase the clinical relevance,
reduce the complexity, and enhance the interpretability of voice quality
assessment. The proposed method utilizes age, sex, and five audio parameters:
jitter, absolute jitter, shimmer, harmonic-to-noise ratio (HNR), and zero
crossing. A classical machine learning approach is employed. The result reveals
that our approach performs similar to state-of-the-art (SOTA) methods, and
outperforms the latent representation obtained by using popular audio
pre-trained models. This approach provide insights into the feasibility of
different feature extraction approaches for voice evaluation. Audio parameters
such as jitter and the HNR are proven to be suitable for characterizing voice
quality attributes, such as roughness and strain. Conversely, pre-trained
models exhibit limitations in effectively addressing noise-related scorings.
This study contributes toward more comprehensive and precise voice quality
evaluations, achieved by a comprehensively exploring diverse assessment
methodologies.

Sound;Machine Learning;Audio and Speech Processing