Abstract
We demonstrate audEERING's sensAI technology running natively on low-resource mobile devices applied to emotion analytics and speaker characterisation tasks. A show-case application for the Android platform is provided, where audEERING's highly noise robust voice activity detection based on LSTM-RNN is combined with our core emotion recognition and speaker characterisation engine natively on the mobile device. This eliminates the need for network connectivity and allows to perform robust speaker state and trait recognition efficiently in real-time without network transmission lags. Realtime factors are benchmarked for a popular mobile device to demonstrate the efficiency, and average response times are compared to a server based approach. The output of the emotion analysis is visualized graphically in the arousal and valence space alongside the emotion category and further speaker characteristics.
| Original language | English |
|---|---|
| Pages (from-to) | 1182-1183 |
| Number of pages | 2 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 08-12-September-2016 |
| State | Published - 2016 |
| Externally published | Yes |
| Event | 17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States Duration: 8 Sep 2016 → 16 Sep 2016 |
Keywords
- Computational paralinguistics
- Emotion recognition
- Mobile application