1. Low Level Cues to Emotion
Julia Hirschberg
CS 4995/6998
8/6/2019

2. Liscombe et al ’05a
Domain: Phone account information, How May I Help You? system
Motivation Improve customer satisfaction
Emotions examined: Negative, non-negative (collapsed from 7 classes)
Corpus: 5690 dialogs, 20,013 user turns
Training-test split: 75% - 25%
ML method: BoostTexter, combines multiple weak classifiers

3. ~80 Features Lexical: bag of words from transcripts 1,2,3grams
Prosodic:
Energy min, max, median, s.d.,
F0 min, max, median, s.d., mean slope
Ratio of voiced frames to total (rate)
Slope after final vowel (turn-final pitch contour)
Mean F0 and energy over longest normalized vowel (accent)
Syllables per second (rate)
mean vowel length
percent internal silence (hesitation)
Local jitter over longest normalized vowel (VQ)
Last 7 are semi-automatically extracted

4. Results Baseline 73.1% (majority) Lexical + prosodic features 76.1%
Lexical + prosodic + dialog act features 77.0%
Lexical + prosodic + dialog act + context 79.0%

5. Dialogue Act (DA) of current turn
Context:
Change in value of prosodic features from n-1 to n and n to n+1
Bag of words from two previous turns
Edit difference between n-1 and n, n and n-2
DAs of n-1 and n-2
DAs of system prompts eliciting n and n-1
Hand-labeled emotion of n-1 and n-2