1. Inducing and Detecting Emotion in Voice Aaron S. Master Peter X. Deng Kristin L. Richards Advisor: Clifford Nass

2. Overview  Experiment Have subject establish neutral baselines by reading the weather. Induce a positive aroused emotion in subjects by showing them a “happy” video and having them do a happy reading and speech. Repeat for a “sad” negative unaroused video and reading and speech after a distracter test..  Analysis Compare subjects to each other in general. Compare subjects to their individual baselines. Detect emotions using individual or group baselines.

3. Key Questions Answered  Can video and readings cause changes in self- reported emotion? Yes, due to PANAS data.  Do detected features of speech correlate with stimuli? Yes, between and within subjects.  Do detected features of speech correlate with self- reported emotion? Yes  What are the distinguishing characteristics of a positive aroused voice versus a negative calm voice? More words per minute, intensity (variations), pitch variation, voiced frames  Does having an individual emotion baseline (a computer “trained” to each subject) help? Yes, greatly.

4. Detailed Procedure  Participant comes into the lab.  Participant reads out loud a neutral article, recorded for data.  Participant silently reads a primer article.  Participant watches an excerpt of “Miracle”  Participant reads an article about the 1980 Olympics, recorded for data  Participant tells the story in own words, recorded for data.  Participant takes PANAS test  Participant takes a distracter test  Participant silently reads a primer article.  Participant watches an excerpt about Ugandan children  Participant reads an article about a sick girl, recorded for data  Participant tells the story in own words, recorded for data.  Participant takes PANAS  Participant gets reward.

5. Stimuli Video clips:  “Miracle” excerpt – Disney film about U.S. hockey victory  “Invisible Children” excerpt – Independent film about child Soldiers Readings:  “Mike Eruzione Stakes the U.S. to a Lead and a Miracle on Ice”  “After Battle with Fatal Disease, Little Girl Dies”

6. Significant differences were found in some features of the voice recordings:  Average loudness  Words per minute speaking rate  Pitch fluctuation  Relative proportion of voiced speech frames Can see the effect of individual baselines in DET curves for above features (lower left curves “better”). Using these features, a neural net classifier estimated emotion with 67% accuracy with general baselines, and 89% for individual baselines. Significant Results for Each Condition

7. Example: Mean Relative Loudness Data (dB) – Individual Baseline

8. DET Curves

11. Psychological Implications  Manipulations affect speech data – when the subjects are not “acting.”  Under investigation: speech data may reflect responses to stimuli that even self reporting does not.  It seems possible to change people’s emotions in a short period of time (less than 10 minutes)

12. Open Questions and Current Directions  Do everyday events have the same effect on speech as manipulative videos and readings?  Which stimulus had a greater affect on the emotional condition?  What about positive unaroused (“pleased”) and negative aroused (“angry”) conditions?  Do non-U.S. citizens respond the same way? Nissan discovered similar manipulation effects on Japanese subjects, but with different significant features.