Pitch Period Estimation Using Autocorrelation Function

When performing speech analysis, the autocorrelation function can be utilized to estimate pitch period. In the pitch period estimation function, two main parameters are required as inputs: the speech filename to be analyzed and the frame shift value. Typically, the frame shift parameter ranges between 5 to 10 milliseconds. The implementation involves calculating the autocorrelation sequence for each frame and identifying the highest peak after the zero-lag point to determine the fundamental frequency. Beyond using autocorrelation for pitch period estimation, alternative methods can be considered for comprehensive speech signal analysis. These approaches include Fourier Transform-based spectral analysis and Linear Predictive Coding (LPC) techniques. Fourier Transform helps in frequency domain analysis by converting time-domain signals to reveal spectral characteristics, while LPC models the vocal tract parameters through linear prediction coefficients. By integrating these methods, researchers can obtain a more thorough understanding of speech signal features and structural properties. Therefore, during speech analysis, we can employ autocorrelation function for pitch period estimation while combining other analytical methods to acquire additional information and results. This comprehensive approach enhances the understanding and analysis capabilities of speech signals, making it applicable to various domains such as speech recognition systems and speech synthesis applications. The code implementation typically includes preprocessing steps like framing, windowing, and normalization before applying the core autocorrelation algorithm.