In this paper, a new corpus named CoLoSS (Cognitive Load by Speech and performance data in a Symbol-digit dual-task) is presented, which contains speech under cognitive load recorded in a learning task scenario. In order to obtain a reference for cognitive load, a dual-task approach was applied, including a visual-motor primary task that required subjects to learn abstract symbol combinations and an auditory-verbal secondary task to measure the load imposed by the primary task. We report the methodology of collecting the speech recordings, constructing the corpus and describe the properties of the data. Finally, effects of cognitive load on prosodic as well as voice quality features are investigated in conjunction with the corpus. In its current version, the corpus is available to the scientific community, e.g., for exploring the influence of cognitive load on speech or conducting experiments for speech-based cognitive load recognition.
@InProceedings{HERMS18.1008, author = {Robert Herms and Maria Wirzberger and Maximilian Eibl and Günter Daniel Rey}, title = "{CoLoSS: Cognitive Load Corpus with Speech and Performance Data from a Symbol-Digit Dual-Task}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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