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A Preliminary Study on the Acoustic Landmark of Voice Quality in Voiceprint Identification— A Study Based on Random Forest and Decision Tree Model
GENG Puyang, SHI Shaopei, GUO Hong, BIAN Xinwei, LU Qimeng, ZENG Jinhua
2022(4):
54-59.
DOI: 10.3969/j.issn.1671-2072.2022.04.007
Objective Voice quality serves as one of the most important features in forensic voice comparison. However, the acoustic evidence to define voice quality type is still under study. This study aims at establishing a method to define voice quality. Method Based on random forest and decision tree model, the current paper investigated the acoustic landmarks of four types of voice quality (i.e., normal, creaky, breathy, and falsetto) using 18 acoustic parameters. Results The random forest analysis received 90.7% accurate results of voice quality classification, and fundamental frequency (F0), duration, HNR, and H1-A3 are salient factors that contributed to the classification. The results of decision tree model showed that the four types of voice quality could be reasonably classified (i.e., accuracy is above 75%) based on three decision nodes (i.e., HNR, F0 mean, and H1-A3). Conclusion A promising result of voice quality classification could be achieved based on F0, HNR, H1-A3, and etc. The application acoustic landmarks of voice quality could be an effective and significant method for forensic voice comparison practic.
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