Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition
Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition
复制标题
DOI：
10.21437/interspeech.2017-887
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Bogdan Vlasenko;Hesam Sagha;N. Cummins;Björn Schuller
中科院分区：
文献类型：
--
作者：
Bogdan Vlasenko;Hesam Sagha;N. Cummins;Björn Schuller
虽然关于情绪识别的研究表明，性别相关分析可以改善情绪分类性能，但男性和女性言语中抑郁表现的潜在差异还没有得到充分的探索。本文对语音对齐的声学特征进行了定性分析，以突出抑郁症表现的差异。具有语音对齐的性别相关特征的性别相关分析被用于基于语音的抑郁识别。本实验研究揭示了抑郁对元音水平特征影响的性别差异。考虑到实验研究，我们还表明，在执行二进制压抑语音识别任务时，一小部分知识驱动的与性别相关的元音级特征可以比最先进的转向级声学特征具有更好的性能。这些预先选择的基于性别的元音级别特征与话轮级别标准化的Open SMILE特征的组合导致了对抑郁症识别的额外改进。
Whilst studies on emotion recognition show that genderdependent analysis can improve emotion classification performance, the potential differences in the manifestation of depression between male and female speech have yet to be fully explored. This paper presents a qualitative analysis of phonetically aligned acoustic features to highlight differences in the manifestation of depression. Gender-dependent analysis with phonetically aligned gender-dependent features are used for speech-based depression recognition. The presented experimental study reveals gender differences in the effect of depression on vowel-level features. Considering the experimental study, we also show that a small set of knowledge-driven gender-dependent vowel-level features can outperform state-of-the-art turn-level acoustic features when performing a binary depressed speech recognition task. A combination of these preselected gender-dependent vowel-level features with turn-level standardised openSMILE features results in additional improvement for depression recognition.