USE OF REGRESSION IN NOISY SPEECH RECOGNITION
Semih Erg · 2013
In this study, we investigated the contribution of the multiple regression to robust noisy speech recognition in improving the recognition rates. Whe n the noisy speech recognition process is carried out; first of all, an Affine Transformation is performed in order to map the feature vectors of noisy speech into those of clean speech. After t ransforming, the recognition step is achieved using the Common Vector Approach (CVA). We used several multiple linear as well as nonlinear regression models to improve the recognition rates by adding non-linear terms into the model during the affine transformation stage. In th e experimental study, the recognition rates of the noisy speech signals with 0 dB, 5 dB, 10dB, and 20 dB Signal-to-Noise Ratio (SNR) values have been obtained. Noisy speech which has 20, 10, 5, and 0 dB SNR is obtained using MATLAB by adding white Gaussian noise on the clean speech taken from the Texas Instruments (TI) Digit Database. Improvements are observed when non-linear terms are introduced into the