%0 Conference Paper %1 hedeshy2023cnvve %A Hedeshy, Ramin %A Menges, Raphael %A Staab, Steffen %B Proc. INTERSPEECH 2023 %D 2023 %K from:hedeshy myown %P 1553-1557 %R 10.21437/Interspeech.2023-201 %T CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions %U https://www.isca-speech.org/archive/interspeech_2023/hedeshy23_interspeech.html %X 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, abstract = {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.}, added-at = {2023-06-09T10:48:42.000+0200}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/292833d3d7c232c1f384a4721c1a6c4c3/analyticcomp}, booktitle = {Proc. INTERSPEECH 2023}, doi = {10.21437/Interspeech.2023-201}, eventdate = {August 20-24}, eventtitle = {Interspeech 2023}, interhash = {6adf2287678455a9fe702bdad6058b80}, intrahash = {92833d3d7c232c1f384a4721c1a6c4c3}, keywords = {from:hedeshy myown}, month = aug, pages = {1553-1557}, timestamp = {2023-10-30T21:05:13.000+0100}, title = {CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions}, url = {https://www.isca-speech.org/archive/interspeech_2023/hedeshy23_interspeech.html}, year = 2023 }