1. 핵심어 검출을 위한 단일 끝점 DTW 알고리즘 Yong-Sun Choi and Soo-Young Lee
Brain Science Research Center and
Department of Electrical Engineering and Computer Science
Korea Advanced Institute of Science and Technology

2. Contents Keyword Spotting Dynamic Time Warping (DTW)
Meaning & Necessity
Problems
Dynamic Time Warping (DTW)
Advantages of DTW
Some conventional types & Proposed DTW type
Experimental Results
Verification of proposed DTW performance
Standard threshold setting
Results of various conditions
Conclusions

3. Keyword Spotting Meaning Necessity
Detection of pre-defined keywords in the continuous speech
Example)
Keywords : ‘open’, ‘window’
Input : “um…okay, uh… please open the…uh…window”
Necessity
Human may say OOV(Out Of Vocabulary), sometimes stammer
But machine only needs some specific words for recognition

4. Problems & Goal Difficulties Goal of process of implementation
End-Point-Detection of speech segment
Rejection of OOVs
of implementation
A big load of calculations
Complex algorithm
Hard to build up a real hardware system
Goal
Simple & Fast Algorithm

5. DTW for Keyword Spotting
Hidden Markov Model (HMM)
A statistical model : need large number of datum for training
Complex algorithm : hard to implement a hardware system
Many parameters : can cause memory problem
Dynamic Time Warping (DTW)
Advantages
Small number of datum for training
Simple algorithm (addition & multiplication)
Small number of stored datum
Weak points
Need EPD process, Many calculations

6. General DTW Process Known both End Points Repetition of searches
Finding corresponding frames

7. Advanced DTW Myers, Rabiner and Rosenberg No EPD Process
Series of small area searches
Global search in one area
Setting next area around the best match point of local area
Reducing amount of calculations but still much
Tested in isolated word recognition

8. Proposal – Shape & Weights
No EPD process
Only one path
Select the best match point and search again at the point
Less computations
Modifying weights
To compensate weight-sum differences
For search
For distance accumulation

9. Proposal – End Point Small search area End condition
Successive local searches
Start search at one point
End condition
When the point is on the last frame of Ref. pattern
Setting up End Point automatically

10. Proposal – Distance Modifying distance
Using differences of pattern lengths
Pattern lengths of same words are similar each other

11. DTW – Computation Loads
3 types

12. Data Base & EX-SET DB SET construction RoadRally Usages
For keyword spotting
Based on telephone channel
Usages
11 keywords (Total 434 occurrences)
40 male speakers read speech (Total 47 min.) in Stonehenge
SET construction
4 sub-set (about 108 keywords / set)
3 set for training , 1 set for test
2 reference patterns / keyword / set

13. Verification Result Isolated Word Recognition Test Set
3 set for training , 1 set for test
Test
Set
Recognition Rate (%)
General DTW
Proposed DTW 1
96.3
98.2 2
100.0
99.1 3
95.4 4
97.2
Avg.
97.5

14. Experimental Setup Assumption Threshold Result presentation
Any frame can be the last frame of keywords
Threshold
To reject OOV
1 threshold / ref.
Standard threshold : no false alarm in training set
Result presentation
ROC (Receiver Operator Characteristic)
X-axis : false alarm / hour / keyword
Y-axis : recognition rate

15. Thresholds Setting & Recognition Rate of Training Set
Training set = Test set (No false alarm)
Keyword
Right
Total %
Mountain 21 40
52.5
Secondary 38
95.0
Middleton 27 37
73.0
Boonsboro 32 39
82.1
Conway 33
82.5
Thicket 30
77.0
Keyword
Right
Total %
Primary 34 40
85.0
Minus 25 39
64.1
Interstate 37
92.5
Waterloo 35
87.5
Retrace 36
90.0
368
434
84.8

16. Result – DTW & HMM
ROC Curve

17. Changing Conditions
No. of Keywords
No. of References

18. Conclusion Proposed DTW Keyword Spotting Advantages Good performance
Simple structure : addition & multiplication (good for hardware)
No EPD processing
Very small computation load
Small stored datum : small memory
Only keyword information
Good performance
Keyword Spotting
Better than HMM in the case of small training datum