Cepstrum Computation for Speech Signals

This speech signal cepstrum computation program enables superior extraction of pitch and formant information through advanced cepstral analysis techniques. Cepstrum serves as a powerful method for speech signal analysis, transforming the signal's spectrum into cepstral coefficients to achieve more accurate identification of pitch and formant characteristics. Pitch represents the fundamental frequency component in speech, while formants correspond to frequency components exhibiting prominent resonance peaks. The implementation typically involves several computational stages: first, applying Fast Fourier Transform (FFT) to convert the time-domain signal to frequency domain, followed by logarithmic compression of the spectral magnitude, and finally performing inverse FFT to obtain the cepstral coefficients. Key algorithmic considerations include proper windowing functions (such as Hamming window) to minimize spectral leakage and liftering techniques to separate vocal tract and excitation components. The program's computational pipeline facilitates comprehensive cepstral analysis, generating detailed information about pitch periods and formant frequencies. With broad applications spanning speech signal processing, speech recognition systems, and speech synthesis technologies, this implementation provides robust parameter extraction essential for modern speech processing applications. The code architecture typically incorporates modular functions for signal preprocessing, spectral transformation, and peak detection algorithms to ensure reliable feature extraction.