1. USE OF IMPROVED FEATURE VECTORS IN SPECTRAL SUBTRACTION METHOD Emrah Besci, Semih Ergin, M.Bilginer Gülmezoğlu, Atalay Barkana Osmangazi University, Electrical and Electronics Engineering Department Batı Meşelik, Eskişehir, Turkey

2. PURPOSE  To improve the recognition rates of the isolated digits in the noisy enviroment.

3. INTRODUCTION  The database used in this study is TI- Digit Database which contains ‘one’, ’two’, ’three’, ’four’, ’five’, ’six’, ’seven’, ’eight’, ‘nine’, ’zero’, ’ow’ pronouncations.  In the study 426 feature vectors are used for the training set and 20 feature vectors are used for the test set.

4. INTRODUCTION  Gaussian White Noise is added to the clean speech signals and noisy speech signals with 0, 5, 10 and 20 dB SNR are obtained.  The Root-Melcep parameters used in this study are obtained from the speech signals by applying variable frame length algorithm. Thus the feature vectors are constructed.

5. INTRODUCTION  Spectral Subtraction Method is used in the cleaning phase and Common Vector Approach (CVA) is used in the recognition phase.

6. PROPOSED STUDY  Variable Frame Length (VFL) Algorithm is the key concept of this study that means the words are divided into specific number of frames, which is 10 in this study.  Thus the parameters belonging to the speech signals are used at the end of the feature vectors instead of random values.

7. PROPOSED STUDY  Another important point is, a filter is applied to the noisy speech signals.

8. PROPOSED STUDY  Common Vector Approach  The feature vectors for a word-class : a 1, a 2, …, a m feature vectors a i Є R n (i = 1, 2, …, m) m : the number of speakers n : the dimension of each feature vector m > n in this study.  This n-dimensional feature space can be divided into (k-1) dimensional orthogonal difference subspace B an (n-(k-1)) dimensional orthogonal indifference subspace B ┴

9. PROPOSED STUDY  Common Vector Approach (continued)  B is spanned by The orthonormal basis vectors u j Є R n for j = 1, 2, …, k- 1 (k-1<n)  B ┴ is spanned by The orthonormal basis vectors u j Є R n for j = k, k+1, …, n

10. PROPOSED STUDY  Common Vector Approach (continued)  The orthogonal projection matrix P onto the difference subspace B  The orthogonal projection matrix P ┴ onto the indifference subspace B ┴  From here, the common vector a com

11. RESULTS Recognition Rates For Cleaned Speech Signals (Spectral Subtraction with 250 Parameters and VFL Algorithm) Table 1. Training SetTest Set 0 dB54.822945.4545 5 dB81.519471.3636 10 dB96.478991.3636 20 dB99.658697.7273

12. RESULTS Recognition Rates For Noisy Speech Signals (Spectral Subtraction with 250 Parameters and VFL Algorithm) Table 2. Training SetTest Set 0 dB 30.046929.5455 5 dB 49.167744.0909 10 dB 71.276160.4545 20 dB 92.104185.4545

13. Spectral Subtraction Recognition Rates For Cleaned Speech Signals Training Set Test Set 0 dB54.845.5 5 dB81.571.4 10 dB96.591.4 20 dB99.797.7 Table 1. 250 Parameters with VFL Algorithm Training Set Test Set 0 dB2528 5 dB5043 10 dB7366 20 dB9480 Table 2. 407 Parameters RESULTS

14. CONCLUSION  Results obtained by using Spectral Subraction with Variable Frame Length (VFL) Algorithm and Common Vector Approach are higher than Spectral Subraction method with 407 parameters.  The results are better because all of the data used belongs to the speech sample and the bandpass filter decreases the effect of the noise.

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