Name
Title | ||
|---|---|---|
Implementing Gender-Dependent Vowel-Level Analysis For Boosting Speech-Based Depression Recognition |
Abstract | ||
|---|---|---|
Whilst studies on emotion recognition show that gender dependent 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.21437/Interspeech.2017-887 | 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION |
Keywords | Field | DocType |
Depression, Gender, Vowel-Level Formants, Speech Motor Control, Classification | Computer science,Speech recognition,Boosting (machine learning),Vowel | Conference |
ISSN | Citations | PageRank |
2308-457X | 1 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bogdan Vlasenko | 1 | 235 | 12.72 |
| Hesam Sagha | 2 | 268 | 15.92 |
| Nicholas Cummins | 3 | 349 | 32.93 |
| Björn Schuller | 4 | 6749 | 463.50 |