Tracking of Multiple Fundamental Frequencies in Diplophonic Voices

Abstract

Diplophonia is a type of pathological voice in which two fundamental frequencies ( $f_o$ ) are present simultaneously. Specialized audio analyzers that can handle up to two $f_o$ s in diplophonic voices are in their infancy. We propose the tracking of up to two $f_o$ s in diplophonic voices by audio waveform modeling (AWM), which involves obtaining candidates by repetitive execution of the Viterbi algorithm, followed by waveform Fourier synthesis, and heuristic candidate selection with majority voting. Our approach is evaluated with reference $f_o$ -tracks obtained from laryngeal high-speed videos of 29 sustained phonations and compared to state-of-the-art tracking algorithms for multiple $f_o$ s. An accurate and a fast variant of our algorithm are tested. The median error rate of the accurate variant is 6.52%, whereas the most accurate benchmark achieves 11.11%. The fast variant is more than twice as fast as the fastest relevant benchmark, and the median error rate is 9.52%. Furthermore, illustrative results of connected speech analysis are reported. Our approach may help to improve detection and analysis of diplophonia in clinical research and practice, as well as to advance synthesis of disordered voices.