1. Automatic Prosodic Event Detection
Julia Hirschberg
GALE PI Meeting
March 23, 2007

2. Introduction
Acoustic/Prosodic features improve speech distillation performance (Maskey & Hirschberg, 2005)
Can categorical features make a contribution?
Pitch accent
Intonational Phrase boundaries

3. Material 20 minutes of manually annotated material from TDT4.
_1830_1900_ABC_WNT
25 hypothesized speaker IDs
3326 words
1658 (49.8%) accented
556 (16.7%) preceed IP boundaries

4. Pitch Accent: Energy Based Voting Classifier
Extract energy features from a set of 210 frequency regions
Frequency ranges from 0-19 bark
Bandwidth ranges from 1 to 20 bark
Construct 210 pitch accent decision tree classifiers using only energy features
Voting yields 83.7% accuracy

5. Pitch Accent: “Corrected” Voting Classifier
Classify each prediction as ‘correct’ or ‘incorrect’ using pitch and duration features.
Requires 210 “correcting” decision tree classifiers
If an energy prediction is hypothesized to be ‘incorrect’, invert it.
“Corrected” Voting yields 88.5% accuracy

6. Intonational Phrase Boundary Detection
Decision Tree Classifier with pitch, intensity and duration features
89.1% accuracy
(68.3% Precision, 64.7% recall)
Most predictive features:
Long following pause length
Descending change in energy over the final 3/4 of the word
Lower minimum energy relative to the 2 preceding words
Decreased standard deviation of pitch

7. Conclusion and Future Work
We can detect pitch accent with high accuracy, but can this information be used to improve distillation?
While we do not detect them with very high accuracy, can even noisy IP boundaries be used to segment BN for extractive summarization?
Are hypothesized IP boundaries useful candidate story segmentation points?

8. Thank You

9. Energy Features Min, max, mean, stdev, rms of energy
For IP boundary detection only:
Min, max, mean, stdev, rms of energy
The above extracted over the final 3/4 of the word
The above extracted over the final 200ms
Range and Z-score normalized max and mean raw energy by contextual window
All combinations of 2,1,0 previous words, and 2,1,0 following

10. Pitch Features
Min, max, mean, stdev, rms of raw and speaker normalized F0 and F0
For IP boundary detection only:
The above extracted over the final 3/4 of the word
The above extracted over the final 200ms
Pitch reset following the current word
Difference between the mean of the last 10 pitch points (10ms frame) of the current word and the first 10 of the following
Range and Z-score normalized max and mean raw and speaker normalized F0 by contextual window
All combinations of 2,1,0 previous words, and 2,1,0 following

11. Duration Features Length of word (in seconds) Pause preceding the word
Pause following the word