Spectral and Temporal Features in Speech signal

There are two types of features of a speech signal :

• The temporal features (time domain features), which are simple to extract and have easy physical interpretation, like: the energy of signal, zero crossing rate, maximum amplitude, minimum energy, etc.
• The spectral features (frequency based features), which are obtained by converting the time based signal into the frequency domain using the Fourier Transform, like: fundamental frequency, frequency components, spectral centroid, spectral flux, spectral density, spectral roll-off, etc. These features can be used to identify the notes, pitch, rhythm, and melody.

The most successful spectral features used in speech are (i) Mel frequency cepstral coefficients (MFCC) and (ii) Perceptive Linear Prediction (PLP) features. It is well known that the basilar membrane in the inner ear actually analyzes the frequency content of the speech we hear. In fact, the analysis of basilar membrane can be modeled by a bank of constant Q, band pass filters. There also exist the critical bands, which give rise to the phenomenon of masking - where one strong tone or burst can mask another weaker tone within the critical band. Actually, both MFCC and PLP capture these characteristics of our auditory system in some way; so, even though it looks strange, the same features give reasonably good performance for speech recognition, speaker recognition, language identification and even accent identification ! However, these spectral features are not very robust to noise.

On the other hand, some of the time domain (temporal) features such as plosion index and maximum correlation coefficient are relatively more robust to noise.