Vocal Breath Sound Based Gender Classification

TL;DR

This study investigates whether vocal breath sounds contain enough gender-specific information for automatic classification, finding that simple features and segment durations can achieve comparable accuracy to more complex methods.

Contribution

It is the first to analyze gender classification using vocal breath sounds with a focus on feature types, segment duration, and classifier complexity.

Findings

Knowledge-based features perform well with low-complexity classifiers.

3-second breath segments are optimal for classification.

Segment location is less critical than duration.

Abstract

Voiced speech signals such as continuous speech are known to have acoustic features such as pitch(F0), and formant frequencies(F1, F2, F3) which can be used for gender classification. However, gender classification studies using non-speech signals such as vocal breath sounds have not been explored as they lack typical gender-specific acoustic features. In this work, we explore whether vocal breath sounds encode gender information and if so, to what extent it can be used for automatic gender classification. In this study, we explore the use of data-driven and knowledge-based features from vocal breath sounds as well as the classifier complexity for gender classification. We also explore the importance of the location and duration of breath signal segments to be used for automatic classification. Experiments with 54.23 minutes of male and 51.83 minutes of female breath sounds reveal that knowledge-based features, namely MFCC statistics, with low-complexity classifier perform comparably to the data-driven features with classifiers of higher complexity. Breath segments with an average duration of 3 seconds are found to be the best choice irrespective of the location which avoids the need for breath cycle boundary annotation.