Speech signals are analyzed using short-time spectral methods and simple frame-based measures under the assumption of local stationarity. Voiced and unvoiced regions are identified using energy and zero-crossing rate, and the fundamental frequency is estimated via autocorrelation in periodic segments.
The spectral envelope is modeled using linear predictive coding (LPC), providing a parametric representation consistent with the source–filter interpretation of speech production. Synthetic signals generated under the same assumptions are used as a reference, allowing direct comparison between modeled and recorded speech.
The results indicate that classical parametric methods capture the dominant spectral structure and excitation behavior of speech, while failing to reproduce fine temporal variation and natural irregularities.
The signal is analyzed under the assumption of short-time stationarity. A frame-based approach is adopted, with analysis performed over fixed-length windows.
Time–frequency structure is examined using the Short-Time Fourier Transform (STFT), with window lengths chosen to illustrate the trade-off between temporal and spectral resolution.
Segmentation into voiced, unvoiced, and silent regions is based on short-time energy and zero-crossing rate. These measures provide a simple distinction between periodic and noise-like components.
The fundamental frequency is estimated via autocorrelation and restricted to plausible values to avoid spurious detections.
The spectral envelope is modeled using Linear Predictive Coding (LPC), consistent with the source–filter representation of speech. Synthetic signals are generated under the same framework and compared with recorded speech.
| Figure | Description |
|---|---|
| Fig. 1 | The waveform shows alternating low- and high-energy regions, motivating short-time analysis. |
| Fig. 2 | The spectrogram illustrates the time–frequency trade-off: short windows resolve transients, while longer windows reveal harmonic structure. |
| Fig. 3 | Energy and zero-crossing rate provide a sufficient, though coarse, separation of voiced and unvoiced regions. |
| Fig. 4 | The estimated fundamental frequency appears only in voiced segments, reflecting periodic excitation. |
| Fig. 5 | The LPC envelope captures the smooth spectral structure consistent with the source–filter model. |
| Fig. 6 | Synthesized speech reproduces the general spectral envelope but lacks the variability of natural speech. |
Modified versions of the speech signal are provided in which pitch and phase have been altered. These retain structural properties required for analysis while reducing speaker-specific information.
These methods provide a consistent and interpretable description of speech under standard assumptions. They capture the principal structure of the signal but do not account for fine temporal variation or natural irregularities.