Voice Quality Assessment by Simulating GRBAS Scoring

This paper is about the assessment of voice quality as required routinely in hospital voice clinics. It describes a computer application capable of analysing recordings of a patient's voice and producing quantitative assessments of its quality, simulating those traditionally made by trained speech and language therapists (SLTs). Adopting a machine learning approach based on a database of recordings and assessments by a team of SLTs required measurements of consistency to be taken into account. The means of doing this, details of the machine learning approaches and the performance of the resulting algorithms are presented

Measurement of Rater Consistency and its Application in Voice Quality Assessments

This paper concerns the assessment of voice quality as required for patients who attend a hospital voice clinic. Clinicians currently make assessments perceptually according to a well known 'GRBAS' scale. Our aim is to use machine learning (ML) to make computerised GRBAS assessments from measurements of features extracted and parameterised using digital signal processing (DSP). The ML is based on assessments by a group of clinicians (raters) and it is useful to measure and take into account the consistency of these assessments. This process has revealed some insight into commonly used techniques for measuring consistency, such as ICC and the Cohen and Fleiss Kappas. Results obtained from the application of these techniques to ML programs for GRBAS assessment are presented