Detection of speaker – dependent features

(P1) Read and write a .wav file, acquire different learning samples of speech for different speakers – write them as .wav files. Perform initial segmentation into phonemes and silence. Make detection of voiced and unvoiced phonemes in the time signal. Present the waveform and the 2-D spectrogram in graphic windows. (P2) Perform the windowed Fast Fourier Transform (FFT). Present the spectrogram image. Detect the maxima for every window – detect the formants - present the formants in the spectrogram image. (P1, P2) Propose and test a normalization procedure for the MFCC feature-detection procedure on base of detected border positions of formants.