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1. (WO2018227169) CONVERSATIONS HOMME-MACHINE OPTIMALES UTILISANT UN DISCOURS NATUREL AMÉLIORÉ GRÂCE À DES ÉMOTIONS UTILISANT DES RÉSEAUX DE NEURONES HIÉRARCHIQUES ET UN APPRENTISSAGE PAR RENFORCEMENT
Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

What is claimed is:

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. Hie 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. Hie 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. Hie 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.