Convolutional Bidirectional Long Short-Term Memory for Deception Detection With Acoustic Features

Despite the widespread use of multi-physiological parameters for deception detection, they have been severely restricted due to the high degree of cooperation in contacting-detection. Therefore, a non-contacting method is proposed for deception detection using acoustic features as an input and convolutional bidirectional long short-term memory (LSTM) as a classifier. The algorithm extracts frame-level acoustic features whose dimension dynamically varies with the length of speech, in order to preserve the temporal information in the original speech. Bidirectional LSTM was applied to match temporal features with variable dimension in order to learn the context dependences in speech. Furthermore, the convolution operation replaces multiplication in the traditional LSTM, which is used to excavate time-frequency mixed data. The average accuracy of the experiment on Columbia-SRI-Colorado corpus reaches 70.3%, which is better than the previous works with non-contacting modes.