Abstract:
Analyzing the cognitive load generated in the brain is the
most important issue for specific applications such as voice
stress analysis (VSA) systems in which the detection of
stressed speech caused by an act of deception under law
enforcement interview questioning or military interrogation.
The most widely used algorithm for VSA systems is the
empirical mode decomposition (EMD). Currently EMD that
uses the cubic spline interpolation technique to find the
envelopes of the non-periodic signal takes a long processing
time, and to achieve accurate results the process is very time
consuming and expensive otherwise some tests tend to
produce inaccurate results. On the other hand EMD that uses
Hilbert analysis method to speed up the process and provide
more accurate results, suffer from finding the envelopes of the
non-periodic signal.
In this paper, a new algorithm is proposed for VSA, named
fast Fourier transform (FFT) with a modified Hilbert analysis
method (MH) for EMD algorithm, (FTT_MH_EMD), which
provides a new technique that modifies the conventional
Hilbert analysis method and combines it with the fast Fourier
transform algorithm to overcome the previous limitations of
using individually the FTT algorithm, the cubic spline
interpolation technique or the conventional Hilbert analysis
method and that can speed up the processing time and gives
accurate results.
Simulations and results witness that the proposed algorithm
provides higher accuracy than the other attempts and also the
processing time has dropped by 10 times faster than those in
the products currently available in the market for VSA The advantages of using FFT for VSA are low cost when
implemented in products and fast iterations method, and the
disadvantage is the low resolution and therefore low accuracy
(60%–70%) [1]. On the other hand the advantage of using
Mcquiston Ford for VSA is the high accuracy results which
are greater than 80%, and the disadvantage are complex
algorithms and expensive implementation [5].
An important, barely audible, frequency is produced by the
human vocal cords which could measure in a fairly accurate
manner if the subject is being deceitful due to the effect the
brain’s cognitive load has on the vocal cords [1 and 5]. This
frequency is called the micro-tremor frequency generated by
the involuntary micro-tremor muscle fibers in the vocal cords.
When the subject is being deceitful, more work is induced on
the brain which as a result causes tremor muscles to tense up
in a non-voluntary manner [1 and 6].
Different non voluntary muscles would tense up including the
micro-tremor muscle fibers found on the vocal cords. This
induces a higher micro-tremor frequency range notably
showing a large quantity of induced cognitive load and
therefore a deceitful subject [7].
The micro-tremor frequency of all muscles in the body,
including the vocal chords, vibrates in the 8 to 12 Hz range in
the normal case without any stress, and that the VSA vendors
claim to be the sole source of detecting if an individual is
lying or is under stress [1, 4 and 8].
The paper is organized from seven sections EMD, EMD with
Hilbert Analysis, previous work and limitations, the proposed
algorithm, the proposed VSA system model, results and
simulations and finally the conclusion