OPTIMAL HUMAN-MACHINE CONVERSATIONS USING EMOTION-ENHANCED NATURAL SPEECH USING HIERARCHICAL NEURAL NETWORKS AND REINFORCEMENT LEARNING
A system and method for emotion-enhanced natural speech using dilated convolutional neural networks, wherein an audio processing server receives a raw audio waveform from a dilated convolutional artificial neural network, associates text-based emotion content markers with portions of the raw audio waveform to produce an emotion-enhanced audio waveform, and provides the emotion-enhanced audio waveform to the dilated convolutional artificial neural network for use as a new input data set.
1 . A system for emotion-enhanced natural speech audio generation using dilated convolutional neural networks, comprising:
an automated emotion engine comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device and configured to provide a plurality of input data to, and receive a plurality of output data from, a dilated convolutional artificial neural network;
wherein the automated emotion engine is configured to receive at least a raw audio waveform from the dilated convolutional artificial neural network; and
wherein the automated emotion engine is configured to recognize a plurality of emotional states within the raw audio waveform.
2 . The system of claim 1 , wherein the automated emotion engine is configured to produce an emotion-enhanced audio waveform by associating a plurality of emotion content markers, each comprising at least a text label describing an emotional state, with at least a portion of the audio waveform.
3 . The system of claim 2 , wherein the automated emotion engine is configured to provide the emotion-enhanced audio waveform to the dilated convolutional artificial neural network as an input data set.
4 . The system of claim 1 , wherein at least a portion of the emotion content markers are based on a text-to-speech script that was used in the generation of the raw audio waveform.
5 . A method for emotion-enhanced natural speech audio generation using dilated convolutional neural networks, comprising the steps of:
receiving, at an automated emotion engine comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a network-connected computing device and configured to provide a plurality of input data to, and receive a plurality of output data from, a dilated convolutional artificial neural network, at least a raw audio waveform from the dilated convolutional artificial neural network;
associating a plurality of text-based emotion content markers with at least a portion of the audio waveform, producing an emotion-enhanced audio waveform; and
optionally providing the emotion-enhanced audio waveform to the dilated convolutional artificial neural network as an input data set for training.