🎙️Micro-Expression and Voice Tone Analysis

Convolutional Neural Networks (CNN)

In Micro-Expression Detection, BitTruth employs CNNs, which are highly effective for image recognition tasks. CNNs are used to analyze facial features and micro-expressions in real-time video content. These neural networks identify minute, involuntary facial movements that occur in response to emotions like fear, anxiety, or excitement, which can often go unnoticed by the human eye but are crucial for detecting hidden emotional states.

WaveNet

To assess voice tone and pitch, BitTruth incorporates WaveNet, a deep learning model developed by Google DeepMind. WaveNet is particularly adept at analyzing the subtle changes in tone, pacing, and inflection of speech, which are key indicators of emotional sincerity or deceit. By evaluating how a person speaks—whether they show signs of stress, nervousness, or confidence—BitTruth can further validate the emotional authenticity of the content.

Recurrent Neural Networks (RNN)

RNNs are used in conjunction with CNNs to analyze temporal patterns in both facial expressions and voice tone. The RNNs help BitTruth track changes in emotions over time, making it possible to understand emotional dynamics within a video or audio clip. This is especially important in detecting shifts in emotional state, such as a sudden change in voice tone or facial tension during a conversation, interview, or video.