The appraisal of voice quality is relevant to the clinical care of disordered voices. It contributes to the selection and optimization of clinical treatment as well as to the assessment of the outcome of the treatment. Levels of description of voice quality include the biomechanics of the vocal folds and their kinematics, temporal and spectral acoustic features, as well as the auditory scoring of hoarseness, hyper- and hypo-functionality, creakiness, diplophonia, harshness, etc. Broad and fuzzy definitions of terms regarding voice quality are in use, which impede scientific and clinical communication.
Aim of the special session is to contribute to the improvement of the clinical assessment of voice quality via a translational approach, which focuses on quantifying and explaining relationships between several levels of description. The objective is to gather new insights, advancement of knowledge and practical tools to assist researchers and clinicians in obtaining effective descriptions of voice quality and reliable measures of its acoustic correlates. Topics of interest include, but are not limited to,
the statistical analysis and automatic classification, possibly relying on state-of-theart machine learning approaches, of distinct types of voice quality via non-obtrusively recorded features
the analysis and simulation of vocal fold vibrations by means of analytical, kinematic or mechanical modelling,
the interpretation and modeling of both acoustic emission and/or high–speed video recordings such as videolaryngoscopy and videokymography,
the synthesis of disordered voices jointly with auditory experimentation involving synthetic and natural disordered voice stimuli.
We plan to have six oral presentations (15 minutes + 5 minutes discussion and changeover). A poster session will be organized if the number of accepted presentations exceeds 6.
Philipp Aichinger (firstname.lastname@example.org)
Abeer Alwan (email@example.com)
Carlo Drioli (firstname.lastname@example.org)
Jody Kreiman (email@example.com)
Jean Schoentgen (firstname.lastname@example.org)