1. The TUM Cumulative DTW
Approach for the Spoken Web
Search Task
Cyril Joder, Felix Weninger,
Martin Wöllmer, Björn Schuller
Institute for Human-Machine Communication
Technische Universität München

2. Summary
• Not a „system“
• Low-level features only
• No ASR
• Little „engineering“
• Method of integrating discriminative
training into DTW
Mediaeval 2012 Workshop 2

3. Cumulative DTW (CDTW)
• Limitations of DTW:
– Only one local cost function (distance)
– Usually manual parameter tuning
• Idea:
– Use different local cost functions for each step
– Automatic learning of these functions as
combination of general features
Mediaeval 2012 Workshop 3

4. From DTW to CDTW
Mediaeval 2012 Workshop 4

5. From DTW to CDTW
Mediaeval 2012 Workshop 5

6. From DTW to CDTW
Mediaeval 2012 Workshop 6

7. Softmax?
Mediaeval 2012 Workshop 7

8. Features
Mediaeval 2012 Workshop 8

9. Decision
• Are the two sequences instances of the
same word/expression?
Decision
• Learning of the parameters.
– Backpropagation (stochastic gradient descent)
– Training data: queries/utterances of dev set
Mediaeval 2012 Workshop 9

10. Search Procedure
Mediaeval 2012 Workshop 10

11. Candidate Search
• Align query with entire
utterance
– CDTW with backtracking
– “Scores” for each point
• Extract potential starts and
ends
– Peak-picking of scores
• Filter by duration
– Only allow warping factors < 2
Mediaeval 2012 Workshop 11

12. Candidate Search
• Align query with entire
utterance
– CDTW with backtracking
– “Scores” for each point
• Extract potential starts and
ends
– Peak-picking of scores
• Filter by duration
– Only allow warping factors < 2
Mediaeval 2012 Workshop 12

13. CDTW Score Post-Processing
• Same decision function as for learning
– Many false positives
– Bias toward some queries
• Heuristic post-processing:
– For each query, subtract a specific threshold
– Threshold: 90-th percentile of the CDTW
scores for that query
Mediaeval 2012 Workshop 13

14. Results
run devQ-devC evalQ-devC devQ-evalC evalQ-evalC
P(miss) 55.6% 59.5% 60.2% 54.5%
P(FA) 1.18% 1.13% 1.17% 1.13%
ATWV 0.263 0.333 0.164 0.290
• Great improvement over naive DTW
– ATWV = 0.065 on devQ-devC
• ATWV scores depend on the run
Mediaeval 2012 Workshop 14

15. Results
• DET curves similar
• CDTW seems to
generalize well
• Decision function has
to be improved
Mediaeval 2012 Workshop 15

16. Conclusion
• CDTW: promising results
– Data-based approach with satisfactory results
– Significantly outperforms (naive) DTW
– Good generalization
• Future work:
– Decision function
– Acoustic descriptors
– Integrate „hard“ path constraints into search
Mediaeval 2012 Workshop 16

17. Thank you.
Cyril.Joder@tum.de
Mediaeval 2012 Workshop 17