1. Keyword Spotting Dynamic Time Warping
Ali Akbar Jabini
Alexandre Mercier-Dalphond
Spring 2006

2. Introduction Speech recognition: Computer can interpret speech
Need input to digitalize sounds
Microphone
People can speak faster than type
Commercial systems available since 1990s
People prefer Physical interactions
Keyboard/Mouse, On/Off switch
Low Accuracy for large vocabulary with noise (50%)

3. Introduction
Speech recognition is more and more used for smaller vocabulary banks
Credit Card Systems
Simple switching commands
Directory assistance
Cheap to implement
High Accuracy
Can verify their interpretation
Idea: speech recognition for household appliances

4. OUTLINE Area of investigation Concrete task/Goal Schematic
Feature extraction
DTW
Training
Evaluation metrics
Conclusion

5. Area of Investigation Keyword Spotting:
Subfield of speech recognition
Grammar constrained
Keyword Spotting in isolated word recognition
Keywords utterances
Keyword separated by silence
Main technique is DTW

6. Concrete task/Goal
Goal: develop a robust speaker independent keyword spotting scheme to operate household appliances
Concrete tasks
Digitalize the sound inputs
Implementation in MatLab
Train the model with the grammar
Analyze the performances of our scheme

7. Schematic
Microphone
A/D
Feature extraction
DTW
Output
Grammar

8. Feature extraction Pre-emphasis Blocking into frames Windowing
Flattening the spectrum of the signal
Blocking into frames
Length of the Fourier Transform
Windowing
Sample window (maybe Hamming)
Mel frequency Cepstral coefficients
More reliable than LPC coefficients
This will be imputed in the DTW algorithm

9. DTW Idea: smallest distance between an input and the training bank
Cepstrum features
Dynamic programming: the time axis his not linear to account for utterances
t0 -> t0+5
t1 -> t1-2

10. DTW

11. DTW

12. Training Need to create our own grammar Use this data with DTW
On: Onnn, Honnn, open, opeeenn
Off: Hooofff, Hoff, offfff, close
As many potential utterances as possible
Use this data with DTW

13. Evaluation metrics Accuracy High noise Low noise Independent speaker
Training data speaker
Would like to obtain 80% or more

14. Conclusion Early stage
No code implemented yet
Many challenges a head
Our methodology may change slightly
There is a big potential market for such technique -> influence on every day life.