  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.
