Toward Language-Agnostic Speech Emotion Recognition

Ntalampiras S. Toward Language-Agnostic Speech Emotion Recognition. J Audio Eng Soc. 2020;68(1/2):7-13. doi:10.17743/jaes.2019.0045

@article{Ntalampiras2020_20713,
  author = {Ntalampiras, Stavros},
  title = {{Toward Language-Agnostic Speech Emotion Recognition}},
  journal = {Journal of the Audio Engineering Society},
  volume = {68},
  number = {1/2},
  pages = {7--13},
  year = {2020},
  month = jan,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2019.0045},
  url = {https://doi.org/10.17743/jaes.2019.0045}
}

TY  - JOUR
TI  - Toward Language-Agnostic Speech Emotion Recognition
AU  - Ntalampiras, Stavros
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 68
IS  - 1/2
SP  - 7
EP  - 13
PY  - 2020
DA  - 2020/01/06
DO  - 10.17743/jaes.2019.0045
UR  - https://doi.org/10.17743/jaes.2019.0045
PB  - Audio Engineering Society
LA  - en
AB  - Cross-language speech emotion recognition is receiving increased attention due to its extensive real-world applicability. This work proposes a language-agnostic speech emotion recognition algorithm focusing on Italian and German languages. The mel-scaled and temporal modulation spectral representations are combined and then subsequently modeled by means of Gaussian mixture models. Emotion prediction is carried out via a Kullback Leibler divergence scheme. The proposed methodology is applied to two problem settings: one including positive vs. negative emotion classification and a second one where all Big Six emotional states are considered. A thorough experimental campaign demonstrated the efficacy of such a method, as well as its superiority over other generative modeling schemes and state-of-the-art approaches. The results demonstrate the feasibility of recognizing emotional states in a language-, gender- and speaker-independent setting.
ER  -