Extraction of LPC Feature Values from Speech Data
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In our graduation project, we work with speech data and extract their Linear Predictive Coding (LPC) feature values. LPC features represent important characteristics for describing speech signals, modeling the vocal tract filter by predicting current speech samples as a linear combination of past samples. Through LPC feature extraction, we can analyze spectral properties of speech signals and enable applications like voice recognition and speech synthesis. The implementation typically involves calculating autocorrelation coefficients, solving the Yule-Walker equations using Levinson-Durbin recursion, and obtaining LPC coefficients that represent the spectral envelope. In our graduation project, we will thoroughly investigate and explore methods for extracting LPC features from speech data, including practical implementation considerations like frame segmentation, windowing, and order selection, to better understand and apply speech signal processing techniques.
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