Short-Term Analysis of Speech Signals

Short-term analysis of speech signals involves the following essential steps to guarantee absolute quality:

- Frame Splitting: Segment the speech signal into short-term frames for subsequent analysis. Typically implemented using overlapping window functions (e.g., Hamming window) with frame lengths of 20-40ms to maintain temporal continuity.

- Short-Term Energy: Compute the energy of each frame to analyze power variations in the speech signal. Implementation involves squaring and summing sample values within each frame using vectorized operations.

- Short-Term Average Magnitude: Calculate the average amplitude per frame to capture magnitude characteristics. Achieved through mean absolute value computation with noise robustness considerations.

- Short-Term Zero-Crossing Rate: Determine the zero-crossing frequency per frame to identify unvoiced segments and high-frequency components. Algorithm counts sign changes between consecutive samples.

- Short-Term Autocorrelation Function: Compute frame-wise autocorrelation to analyze periodicity and pitch information. Implemented using lag-based correlation calculations with FFT optimization for efficiency.

- Short-Term Magnitude Difference: Measure frame-level magnitude variations to detect abrupt changes. Calculated as the sum of absolute differences between consecutive samples within frames.

- Cepstrum: Perform cepstral transformation to obtain quefrency-domain coefficients. Implemented through FFT → log magnitude → inverse FFT pipeline for formant and pitch separation.

- Complex Cepstrum: Execute complex cepstrum analysis using phase unwrapping algorithms to preserve phase information for advanced signal reconstruction.

- LPC Coefficients: Compute Linear Predictive Coding coefficients through Levinson-Durbin recursion for vocal tract modeling and prediction error minimization.

- LPC Spectral Estimation: Derive spectral envelopes using LPC coefficients via all-pole filter modeling, providing efficient parameterization for speech recognition systems.

These foundational programs, assigned by my supervisor after securing postgraduate admission, enable comprehensive understanding and processing of speech data through systematic short-term analysis methodologies.