MATHEMATICAL ANALYSIS OF SPEECH FEATURE EXTRACTION FROM RAW WAVEFORMS: FROM SPECTRAL REPRESENTATIONS TO WAV2VEC 2.0

This analytical study formalizes the transformation of a sampled speech waveform into machine-usable acoustic features. The classical path is expressed through short-time framing, Hamming windowing, Fourier analysis, Mel filter banks and MFCCs, while the learned path is examined using wav2vec 2.0. A formula-based derivation shows that the seven-layer wav2vec 2.0 convolutional encoder has an overall stride of 320 samples and a receptive field of 400 samples; at 16 kHz these correspond to 20 ms feature spacing and 25 ms acoustic context. Thus, classical and learned front ends operate on comparable local time scales but differ in whether the transformation is fixed by signal-processing assumptions or learned from unlabeled speech.