MATLAB Implementation of Speech Recognition Preprocessing

In this document, we will discuss how to implement speech recognition preprocessing in MATLAB. Speech recognition preprocessing is a crucial step that enhances the accuracy and reliability of speech recognition systems. The key implementation steps include endpoint detection, pre-emphasis, and windowing techniques. Endpoint detection involves identifying the start and end points of speech signals within an audio recording, which is essential for subsequent signal processing stages. In MATLAB implementation, this typically utilizes energy-based algorithms or zero-crossing rate calculations to distinguish speech segments from silence. Pre-emphasis is implemented as a simple digital filter that balances high-frequency and low-frequency components in speech signals, thereby improving signal clarity. The standard implementation uses a first-order FIR filter with the transfer function H(z) = 1 - αz⁻¹, where α typically ranges between 0.95 and 0.97. Windowing involves segmenting the speech signal into shorter time frames, which is fundamental for subsequent feature extraction. Common implementations use overlapping windows (such as Hamming or Hanning windows) with frame durations of 20-30 milliseconds and overlap percentages of 50-75%. This framing process enables stationary analysis of quasi-stationary speech signals. By implementing these key preprocessing steps in MATLAB, developers can gain deeper understanding of speech recognition fundamentals and establish a solid foundation for advanced speech recognition applications. The code implementation typically involves signal processing toolbox functions while allowing customization of parameters like frame size, overlap percentage, and filter coefficients for optimal performance.