Time-Domain Feature Extraction for Speech Signals

In MATLAB programming, various methods can be implemented for time-domain feature extraction of speech signals. Frame splitting involves dividing the speech signal into short-time segments using windowing functions like Hamming or Hanning windows with typical frame lengths of 20-40ms and 50% overlap. Zero-crossing rate (ZCR) measures the signal's waveform variation frequency, calculated by counting sign changes between consecutive samples, which helps analyze speech prosody and pitch characteristics through functions like zcr = sum(abs(diff(sign(frame))))/(2*length(frame)). Short-term energy quantifies signal amplitude variations using energy = sum(frame.^2) or RMS calculations, crucial for detecting voicing segments and intensity changes. Spectrogram generation employs Short-Time Fourier Transform (STFT) with MATLAB's spectrogram() function, visualizing frequency components through time-frequency representations using color-mapped power spectral density. These feature extraction techniques provide comprehensive analysis of speech signal characteristics, enabling applications in speech recognition, emotion detection, and audio processing systems through systematic frame-based processing approaches.