Regression tree approach to studying factors influencing acoustic voice analysis.

Multiple factors influence voice quality measurements (VQM) obtained during an acoustic voice assessment including: gender, intrasubject variability, microphone, environmental noise (type and level), data acquisition (DA) system, and analysis software. This study used regression trees to investigate the order and relative importance of these factors on VQM including interaction effects of the factors and how the outcome differs when the acoustic environment is controlled for noise. Twenty normophonic participants provided 20 voice samples each, which were recorded synchronously on five DA systems combined with six different microphones. The samples were mixed with five noise types at eight signal-to-noise ratio (SNR) levels. The resulting 80,000 audio samples were analyzed for fundamental frequency (F(0)), jitter and shimmer using three software analysis systems: MDVP, PRAAT, and TF32 (CSpeech). Fifteen regression trees and their Variable Importance Measures were utilized to analyze the data. The analyses confirmed that all of the factors listed above were influential. The results suggest that gender, intrasubject variability, and microphone were significant influences on F(0). Software systems and gender were highly influential on measurements of jitter and shimmer. Environmental noise was shown to be the prominent factor that affects VQM when SNR levels are below 30 dB.