Abstract
In this paper, we will examine the “speaker independent” performance of speech emotion recognition systems. The notion of independence suggests that irrespective of the personal attributes of the speaker–race, gender, age–and a reasonably clear speech, with little or no noise or distraction, then two different systems should give the same output. The difference in the systems lies in the use of training data for recognising emotions, algorithms for recognising human speech, and the spectral, prosodic, and quality attributes of the speech in the training data base. We describe the statistically significant differences between outputs of two major speech emotion recognition systems (SERS) - OpenSmile and Vokaturi. Both these systems were trained on posed training data. Our data sample comprised spontaneous speech data of politicians and their spokespersons – 71 people’s speech with an elapsed time of around 16.66 h. We have focused on speeches delivered by the politicians and the statements made by spokespersons; in some cases these may be answers to questions by journalists. There were differences due to the age and the race of our politicians and spokespersons. Even if we ignore the vagaries of spontaneous speech, the differences between the outputs of two major SERS indicates that (a) on the theoretical level, speaker independent claims of such systems do not match, and (b) on the practical level, efforts should be made to widen the training data to include a variety of races and ages and equally importantly to evaluate which of the spectral, prosodic and quality features should be used as the proxy for emotions.
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Notes
- 1.
In the current work, age is approximated: ongoing work related to this and a larger corpus that includes the works sampled here is enhanced by determining the age in years and months of the speaker in each recording at the time of the event recorded.
- 2.
We adopt distinctions as defined by the US Census Bureau [https://www.census.gov/topics/population/race/about.html - last verified 12/09/2022].
- 3.
Extremely low p-values are treated as zeros by the Python packages that are being used here.
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Acknowledgments and Contributions
Khurhsid Ahmad is the Principal Investigator on this project, Carl Vogel is a co-principal and has designed the statistical tests for the reported comparisons, Deepayan Datta and Wanying Jiang are post-graduate students working on our project. We would like to thank Subishi Chemmarathil for her help in curating the database.
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Datta, D., Jiang, W., Vogel, C., Ahmad, K. (2023). Speech Emotion Recognition Systems: A Cross-Language, Inter-racial, and Cross-Gender Comparison. In: Arai, K. (eds) Advances in Information and Communication. FICC 2023. Lecture Notes in Networks and Systems, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-031-28076-4_28
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