Short-Time Analysis of Speech Signals

The text highlights that speech signals are time-varying, with individual parameter changes occurring more gradually than the signal itself. Therefore, measuring these parameters requires a lower sampling frequency than the original signal's sampling rate. By applying window functions to weight and segment the signal in the time domain, local signal sequences can be effectively measured. For precise measurement objectives, two critical dimensions of short-time analysis must be defined: window length (duration of the weighted signal segment) and measurement interval (frame rate, representing the distance between consecutive windows).

Key short-time analysis operations include:

- Short-time energy: This represents the intensity of speech signals and reflects amplitude variations. In implementation, this is typically computed by summing squared signal values within each window frame using overlapping window techniques.

- Short-time autocorrelation function: The autocorrelation function identifies periodicity within signals and serves as foundation for numerous spectral analysis methods. Algorithmically, this involves computing correlation coefficients between windowed signal segments at different time lags.

- Short-time zero-crossing rate: Defined as the rate of sign changes within a given time window, this metric captures signal frequency characteristics. Code implementation typically counts zero-crossing events while applying thresholding to ignore minor fluctuations.

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