1. Two-Stage Mel-Warped Wiener Filter SNR-Dependent Waveform Processing
Anshu Agarwal and Yan Ming Cheng, ASRU 1999
Human Interface Lab, Motorola Labs, USA
Dusan Macho and Yan Ming Cheng, ICASSP 2001
2004/08/17
Presented by Chen-Wei Liu

2. Outline Introduction Two-stage Wiener Filter SNR Waveform Processing
Formula
Algorithm
SNR Waveform Processing
Idea
Experiments

3. Introduction The problem investigated here is that of
speech recognition in an automobile noise environment, where colored noise with intensity as high as or even higher than the input speech is the main characteristic
The performance of conventional speech recognizers
Degrades by more than 50% in typical automobile noise conditions
The automobile noise can be considered as additive
Because it originates from the car’s engine, an opened window, etc.
Many techniques were proposed to subtract the noises from a noise speech signal

4. Introduction It’s believed that This paper proposes an new approach
There is a direct correlation between speech signal strength and speech recognition accuracy
The cleaner the signal, the better the performance
This paper proposes an new approach
Based on the Mel-warped Wiener filter concept
Step 1: coarsely reduce the noise and whiten residual noise
Step 2: wipe the residual noise
By exploiting the correlation characteristics between the speech signal and the white noise

5. Formulation of Mel-Wapred Wiener Filter
The noisy signal with additive noise assumption can be expressed as follows
A Wiener filter is constructed as

6. Formulation of Mel-Wapred Wiener Filter
The mel-warped spectral transfer function of Wiener filter is expressed as
Where m stands for mel-frequency and the warping function
The process of computing the mel-warped power spectrum from an auto-correlation series as
Mel-DCT

7. Formulation of Mel-Wapred Wiener Filter
Wiener filtering is performed in the time domain, where noisy signal convolves with the impulse response of the Wiener filter
We refer to the process of converting a mel-warped transfer function to a time-domain impulse response as inverse Mel-IDCT

8. Two-Stage Filtering The approach is to adapt the estimate in time
Based on a silence-speech detector to capture the evolution of the noise spectrum
First stage
Whitens the noise while preserving the speech spectrum unharmed
Second stage
Wipes out the residual white noise by exploiting the auto-correlation characteristics of white noise

9. Two-Stage Filtering

10. System Overview

11. Basic Idea of SWP
The interference noise energy generated by outside sources is relatively constant within the speech period
Therefore, SNR is variable
If we can locate the high SNR period portion and increase its energy or, vice versa…
The overall SNR of given voiced speech segment is enhanced
A front-end based on the SNR-enhanced signal is expected to be more robust

12. Algorithm Description
In SWP, for each frame
A smoothed instant energy contour is first computed
By using Teager energy operator to obtain the instant energy value at each sample
The contour of voiced sounds has quasi-periodic property
For unvoiced sounds, a flatter contour can be observed
Peaks of the smoothed energy contour (maxima) are located by a simple peak-picking strategy
A window function w(n) is applied to each frame
A rectangular unit window of width w is placed between each two adjacent maxima within the frame

13. Waveform within a clean speech

14. Frame with SNR equal to 0dB

15. Algorithm Description
Next, the portions selected by windowing function are weighted more than the not selected (low SNR portions)
The original waveform within each frame is modified by the following

16. Relationship between Both
The fundamental weakness is that
The interference noise should be sufficiently low to ensure correct maximum
SWP should be applied after 2MWF, which would have already enhanced the SNR to the adequate level

17. Database There are two training scenarios in AURORA2
MCT : multi-condition training
Using both multiple noise types and SNR levels
CST : clean speech training
Only clean speech is involved in training
Within each training scenarios, 3 kinds of testing are performed
A : data are matched in channel effect and noise type
B : data are matched only in channel effect
C : channel mismatch is introduced

18. Experiment One on SWP

19. Experiment Two on SWP

20. Experiment Three on SWP