Automatic Rating of Hoarseness by Text-based Cepstral and Prosodic Evaluation

Abstract

Abstract.The standard for the analysis of distorted voices is perceptual rating of read-out texts or spontaneous speech. Automatic voice evaluation, however, is usually done on stable sections of sustained vowels. In this paper, text-based and established vowel-based analysis are compared with respect to their ability to measure hoarseness and its subclasses. 73 hoarse patients (48.3 ± 16.8 years) uttered the vowel /e/ and read the German version of the text "The North Wind and the Sun". Five speech therapists and physicians rated roughness, breathiness, and hoarseness according to the German RBH evaluation scheme. The best human-machine correlations were obtained for measures based on the Cepstral Peak Prominence (CPP; up to |r | = 0.73). Support Vector Regression (SVR) on CPP-based measures and prosodic features improved the results further to r ≈ 0.8 and confirmed that automatic voice evaluation should be performed on a text recording.

Automatic Evaluation of Voice Quality Using Text-Based Laryngograph Measurements and Prosodic Analysis

“…The position of the voice offset within a word (F0OffPosW), which did not occur in the roughness and breathiness modeling, is a nonnegligible factor for hoarseness evaluation. This has already been detected in a previous study with chronically hoarse persons who were evaluated by five voice experts [ 18 ]. The reason is very probably the F 0 detection algorithm and its decisions regarding voiced and unvoiced sections again.…”

“…This has already been detected in a previous study with chronically hoarse persons who were evaluated by five voice experts [18]. The reason is very probably the F 0 detection algorithm and its decisions regarding voiced and unvoiced sections again.…”

“…However, noisy speech may result in octave errors, that is, instead of the real fundamental frequency the double, triple, or half of the actual value is found. More “noisy” speech influences the F 0 trajectory and thus the correlation with the subjective results [ 18 ].…”

“…The analysis of these aspects has been subject of our working group in the field of automatic speech processing and understanding (identification of what was said and what it means) and also in automatic evaluation of voice and speech disorders (computer-based analysis of voice quality and speech properties, such as intelligibility). This analysis is achieved by a program package called the prosody module [ 16 – 18 ]. The goal of this work is to identify a computer-based equivalent for the subjective ratings of roughness, breathiness, and hoarseness from speech recordings, which are representative for communication by voice.…”

Feature enhancement for classifier optimization and dimensionality reduction

“…Perturbation parameters like jitter are based on F0. In severe dysphonia the phonation is irregular thus it is difficult to detect F0 [23]. This disadvantage is overcome by using CPP which represent spectral noise [8] The measure H2H4 is calculated as spectral energy difference (in dB) between the second and fourth harmonic.…”