Speech Analysis using Relative Spectral Filtering (RASTA) and Dynamic Time Warping (DTW) methods - Zulkifly, Muhammad Amirul Azzim (Universiti Teknologi PETRONAS, 2017)
This work consists of analysis of speech using RASTA and DTW methods. The analysis is based on the speech recognition. Speech recognition converts identified words or speech in spoken language into computer-readable format. The first speech recognition has been developed in the year of 1950s. The variation of speech spoken by individual becomes the main challenge for the speech recognition. Speech recognition has application in many areas such as customer call centers and as a medium in helping those with learning disabilities. This work presents an analysis of speech for Malay single words. There are three stages in speech recognition which are analysis, feature extraction and modeling. The Relative Spectral Filtering (RASTA) is used as the method for feature extraction. RASTA is a method that subsidized the undesirable and additive noise in speech recognition. Dynamic Time Warping (DTW) method is used as the modelling technique.