Reaction tests are typical tests from the field of psychological research and communication science in which a test person is presented some stimulus like a photo, a sound, or written words. The individual has to evaluate the stimulus as fast as possible in a predefined manner and has to react by presenting the result of the evaluation. This could be by pushing a button in simple reaction tests or by saying an answer in verbal reaction tests. The reaction time between the onset of the stimulus and the onset of the response can be used as a degree of difficulty for performing the given evaluation. Compared to simple reaction tests verbal reaction tests are very powerful since the individual can simply say the answer which is the most natural way of answering. The drawback for verbal reaction tests is that today the reaction times still have to be determined manually. This means that a person has to listen through all audio recordings taken during test sessions and mark stimuli times and word beginnings one by one which is very time consuming and people-intensive. To replace the manual evaluation of reaction tests this article presents the REACTION ( Reaction Time Determination) system which can automatically determine the reaction times of a test session by analyzing the audio recording of the session. The system automatically detects the onsets of stimuli as well as the onsets of answers. The recording is furthermore segmented into parts each containing one stimulus and the following reaction which further facilitates the transcription of the spoken words for a semantic evaluation.
[1]
Charles N. Cofer,et al.
Associative indices as measures of word relatedness: A summary and comparison of ten methods
,
1963
.
[2]
S. Dixon.
ONSET DETECTION REVISITED
,
2006
.
[3]
Colin M. Macleod,et al.
Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention
,
2000,
Trends in Cognitive Sciences.
[4]
Yoshihiko Hayashi,et al.
Audio source segmentation using spectral correlation features for automatic indexing of broadcast news
,
2004,
2004 12th European Signal Processing Conference.
[5]
Fausto Pellandini,et al.
Automatic sound detection and recognition for noisy environment
,
2000,
2000 10th European Signal Processing Conference.
[6]
Victor Zue,et al.
Automatic transcription of general audio data: effect of environment segmentation on phonetic recognition 1
,
1997,
EUROSPEECH.
[7]
Thomas Sikora,et al.
Automatic segmentation of speakers in broadcast audio material
,
2003,
IS&T/SPIE Electronic Imaging.