Haptic communication system using cutaneous actuators for simulation of continuous human touch
摘要
說明書
The unsupervised learning module 1300 receives 1336 a training set of speech signals, such as the speech signal 1302. The unsupervised learning module 1300 pre-processes 1338 the training set of speech signal into pre-processed audio data. The pre-processed audio data may include at least a spectrogram, such as the spectrogram 1306. The unsupervised learning module 1300 trains 1340 a neural network using the pre-processed audio data. Additionally, the neural network generates 1342 a sequence of haptic cues corresponding to the speech signal. The neural network has a cost function based on a reconstruction error and a plurality of constraints, such as the constraints 1312. These constraints cause the sequence of haptic cues that are output to have high compressibility and low entropy. The sequence of haptic cues are transmitted cutaneous actuators to generate a sequence of haptic outputs. These cutaneous actuators face a skin surface, such as forearm surface, of a user's body.
Speech Algorithm to Apply Haptic Communication Patterns Related to Consonant-Vowel Pairs or Syllables
