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CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions

Ramin Hedeshy, Raphael Menges, Steffen Staab

, 2023.


Non-verbal voice expressions (NVVEs) have been adopted as a means of human-computer interaction in research studies. However, exploring non-verbal voice-based interactions has been constrained by the limited availability of suitable training data and computational methods for classifying such expressions, leading to a focus on simple binary inputs. We address this issue with a new dataset containing 950 audio samples comprising 6 classes of voice expressions. The data were collected from 42 speakers who donated voice recordings. The classifier was trained on the data using features derived from mel-spectrograms. Furthermore, we studied the effectiveness of data augmentation and improved over the baseline model accuracy significantly with a test accuracy of 96.6% in a 5-fold cross-validation. We have made CNVVE publicly accessible in the hope that it will serve as a benchmark for future research.



@inproceedings{hedeshy2023cnvve, title = {CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, year = {2023}, added-at = {2023-06-09T10:48:42.000+0000}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27c77d415becf2f5405d6520cb66a3548/analyticcomp}, eventdate = {August 20-24}, eventtitle = {Interspeech 2023}, interhash = {6adf2287678455a9fe702bdad6058b80}, intrahash = {7c77d415becf2f5405d6520cb66a3548}, keywords = {myown from:hedeshy}, timestamp = {2023-06-09T10:50:55.000+0000} }