Moving Towards Non-Binary Gender Identification Via Analysis of System Errors in Binary Gender Classification

This paper aims to analyse human perceptions of gender in speech signals, focusing on signals that are misclassified by methods for binary gender classification, looking at the features of speech signals that are more likely to be misclassified, or classified as either nonbinary or unclassifiable. The paper also analyses how human subjects perform in classifying such speech signals to gain insight into differences between machine and human performance levels. It is shown that gender classification systems and human ratings lack inter-annotator agreement, as do human ratings considered individually. There is also discussion of the suitability of continuing to use a binary system for gender in the field. This work fits into a larger body of research ongoing in the area of speech technology for trans-gender voice therapy.