1. Speech / Non-speech Detection.

2. 2005/2/16 Presented by Chen-Wei Liu
Feature Extraction Combining Spectral Noise Reduction and Cepstral HEQ for Robust ASR J.C Segura, M.C. Benitez, A. de la Torre, A.J. Rubio Granada, SPAIN, ICSLP, 2002
2005/2/16
Presented by Chen-Wei Liu

3. Outline The combination of Speech / Non-speech Detection
Spectral Subtraction (SS) and Histogram Equalization (HEQ)
Speech / Non-speech Detection
Order Statistics (OS) Filters
Q(0.9) B
QSNR
Frame Dropping (FD)
HEQ
Experimental Results

4. Block Diagram of the System.

5. NS and SS
Spectral Subtraction (SS)
Noise Estimation (NS)

6. Speech / Non-speech Detection
Order Statistics Filters
Being applied to the log energy of the signal
The first one is a median filter used to track the background noise level (B)
The other one is used to track the signal level and defined as Q(0.9)
The implementation of these two OS filters is based on
The sequence of sorted values of log energy

7. Speech / Non-speech Detection.

8. FD and HEQ Frame Dropping HEQ
It removes every frame in the middle of a window of 11 non-speech frames
As a result, the maximum length of speech pauses allowed after the FD is 10 frames (100 ms)
HEQ
The compensation of SS is not perfect and a residual mismatch still remains
Channel mismatch distortion is not removed by SS at all
In this paper, the reference PDF is Gaussian and applied in cepstrum domain

9. Experimental Results Speech databases and setup Aurora 2.0 Aurora 3.0
Airport, Babble, Car, Exhibition, Street, Subway, Restaurant, Train
3 test sets A,B,C
2 models of Clean-condition and Multi-condition
Aurora 3.0
Recorded in car environment
Two microphones (close-talking and hands-free)
Finnish, Spanish, German
Well-matched, Medium-mismatch, High-mismatched

10. Experimental Results .

11. Experimental Results .

12. Experimental Results
SS+HEQ
SS+FD+HEQ