Formant Estimation of Speech Signals Using Linear Prediction Methods

In this research, we employed linear prediction methods to achieve formant estimation of speech signals. Linear prediction is a fundamental signal processing technique that analyzes formants in speech signals, thereby enhancing our understanding of vocal characteristics and sound production mechanisms. Through linear predictive analysis (LPA) of speech signals, we can accurately estimate formant positions and amplitudes using key algorithmic steps: first computing LPC coefficients through autocorrelation or covariance methods, then solving the polynomial roots to identify formant frequencies. This typically involves implementing functions like lpc() in MATLAB or equivalent Python libraries (e.g., scipy.signal) to derive predictor coefficients, followed by root-finding algorithms to extract formant information from the vocal tract filter. The results provide critical data for speech recognition, speech synthesis applications, and contribute significantly to improving speech processing algorithms and enhancing speech signal quality.