Speech Signal Analysis and Modelling - Digital Speech: Coding for Low Bit Rate Communication Systems - page 55

4
Speech Signal Analysis
and Modelling
4.1 Introduction
The speech signal has been studied for various reasons and applications by
many researchers for many years. Some studies broke down the speech signal
into its smallest portions called phonemes. Here, we will describe the speech
signal in terms of its general characteristics. Speech signals can be classified
into voiced or unvoiced . A voiced speech segment is known by its relatively
high energy content but, more importantly, it contains periodicity which is
called the pitch of voiced speech. The unvoiced part of speech on the other
hand looks more like random noise with no periodicity. However, there are
some parts of speech that are neither voiced nor unvoiced, but a mixture of the
two. These are usually called the transition regions, where there is a change
either from voiced to unvoiced or unvoiced to voiced. The amplitude versus
time plots of typical voiced and unvoiced speech are shown in Figure 4.1
(Note: The unvoiced sound has been amplified five times).
In some speech coding schemes the frequency domain representation of the
speech signal is necessary. For this purpose, the short-time Fourier transform
is very useful. The short-time spectral transformation is also important to
look at a segment of the speech signal and determine features that are not
obvious from the time domain representation.
4.2 Short-Time Spectral Analysis
The short-time Fourier transform plays a fundamental role in frequency
domain analysis of the speech signal. It is used to represent the time-
varying properties of the speech waveform in the frequency domain. A