AMPLITUDE INDEPENDENT FEATURE EXTRACTION FOR EFFECTIVE SPEECH RETRIEVAL - CHAUDHARY, PULKIT (2014)
The performance of speech retrieval systems is measured by its efficiency, accuracy and noise robustness. In existing approaches, the acoustic features are extracted from the power spectrum of the speech signals. These acoustic features are extracted from the amplitudes of the power spectrum of a speech signal. It leads the system to be dependent on the amplitudes for feature extraction. This amplitude dependency is one of the major limitations, because these amplitudes can be easily varied by the quality of input device, microphone position and in the presence of environmental noise, which degrade the system performance. To overcome this problem, a novel approach of amplitude independent feature extraction for noise robust speech retrieval is presented. Singular values are used as acoustic features in this approach which are extracted using singular value decomposition. These singular values are amplitude independent and noise robust features. This work is motivated by the fact that, when a signal is SVD transforms its singular values decrease abruptly with rank increment and data takes a form in which first singular values contains the maximum amount of signal data