Detection of deception by speech signal

• We expect to detect stress in speech by analyzing the change in microtremor frequency of the speaker’s voice.
• For this purpose we have found a program which uses Empirical Mode Decomposition (EMD), a method that has been shown to be effective for the purpose of detecting stress in a person’s voice.

EMD Process

• Empirical Mode Decomposition (EMD) decomposes the original signal into a finite number of intrinsic mode functions (IMFs).
• IMFs are time-varying mono-component (single frequency) functions. The signal is decomposed into IMFs in such a manner that the highest frequency component of each event in the signal is captured by the first IMF.

An IMF should satisfy two conditions:

1. The upper and lower envelope has to be symmetric;
2. The number of zero-crossings and the number of extrema are exactly equal or they differ at most by one.

Once the decomposition is finalized, a real world signal can be mapped as:

Where:

c_i [k] = set of IMFs

r[k] = trend within the data (also referred to as the last IMF or residual)

Detecting stress induced signals

• The second to last IMF is considered the microtremor frequency, unless the total number of IMFs are less than 3, where it is considered the last IMF. 
• If the tremor frequency lies in the range of 8-12 Hz, it is a considered a stress response, while a frequency outside this range is considered a stress response.