Feature Extraction of Speech Signals

The article highlights several key aspects: feature extraction from speech signals, methods for calculating Mel Frequency Cepstral Coefficients (MFCC), principles of linear prediction for speech signals, and derivation of Linear Predictive Coding (LPC) features. To discuss these topics comprehensively, we can examine each point in technical detail.

First, speech signal feature extraction refers to the process of extracting key information that characterizes acoustic properties from speech signals. These features may include spectral characteristics, vocal tract features, and other discriminative parameters. In speech recognition and processing applications, feature extraction serves as a critical preprocessing step that enables differentiation between various speech patterns. Typical implementation involves framing the signal using overlapping windows (e.g., 25ms frames with 10ms overlap) and applying spectral analysis through FFT operations.

Second, Mel Frequency Cepstral Coefficients (MFCC) represent a widely adopted acoustic feature representation method. This technique transforms the speech spectrum to the Mel frequency scale—a perceptual scale approximating human auditory response—and applies cepstral analysis to obtain coefficients capturing vocal tract characteristics. The standard implementation pipeline includes: pre-emphasis, framing, windowing (Hamming window), FFT, Mel-filterbank application, logarithm computation, and finally DCT to decorrelate the coefficients. These coefficients are extensively utilized in speech recognition, speaker identification, and audio classification tasks.

Next, the linear prediction principle constitutes a model-based approach for speech signal modeling and analysis. It operates on the assumption that current speech samples can be linearly predicted from previous samples, enabling efficient parametric representation of speech signals. The core algorithm involves solving the Yule-Walker equations through Levinson-Durbin recursion to determine predictor coefficients that minimize the prediction error. This principle finds broad applications in speech coding, synthesis, and analysis where computational efficiency is paramount.

Finally, Linear Predictive Coding (LPC) features represent an acoustic parameterization method based on linear prediction principles. By performing linear prediction analysis on speech signals, LPC derives a set of filter coefficients that model the vocal tract transfer function. The technical implementation typically involves autocorrelation calculation, solution of linear equations using Levinson-Durbin algorithm, and extraction of 10-16 LPC coefficients that compactly represent speech spectral envelope. LPC features are extensively employed in speech coding standards, voice synthesis systems, and low-bitrate speech transmission applications.

In summary, the article addresses speech signal feature extraction, MFCC computation methodologies, linear prediction theory, and LPC feature derivation. These concepts form fundamental components in speech signal processing, and deeper understanding of their technical specifics and implementation approaches facilitates advanced comprehension of speech processing principles and technologies.