Joint Gender-, Tone-, Vowel- Classification Via Novel Hierarchical Classification for Annotation of Monosyllabic Mandarin Word Tokens

Abstract

The automatic annotation of Mandarin monosyllabic audio word tokens remains an important yet challenging issue in phonetics research. In this work, we address this annotation task via a novel subcategories-classification framework that not only performs word identification via the joint classifications of vowel and tone subcategories, but also performs gender discrimination of the speaker, which stands in contrast to previously proposed methods for Mandarin speech that focused only on tone-, vowel-, or gender- classification. We also propose a novel hierarchical classification algorithm to boost overall classification performance. Extensive experimental results show that our approach yielded superior performance in both cases of adequate and very limited training data. When trained using data from only one female and one male speaker, our approach also yielded the best classification accuracy in all subcategories of the token annotation problem, achieving an Fl-score of 0.742 as opposed to 0.705 as achieved by the second competing approach.