Electronic Arts Inc.

Dhaval Shah

Navid Aghdaie

Mohsen Sardari

Kazi Zaman

Kilol Gupta

Zahra Shakeri

Siddharth Gururani

Jervis Pinto

Kazi Atif-Uz Zaman

Harold Henry Chaput

John Kolen

Igor Borovikov

A system for use in video game development to generate expressive speech audio comprises a user interface configured to receive user-input text data and a user selection of a speech style. The system includes a machine-learned synthesizer comprising a text encoder, a speech style encoder and a decoder. The machine-learned synthesizer is configured to generate one or more text encodings derived from the user-input text data, using the text encoder of the machine-learned synthesizer; generate a speech style encoding by processing a set of speech style features associated with the selected speech style using the speech style encoder of the machine-learned synthesizer; combine the one or more text encodings and the speech style encoding to generate one or more combined encodings; and decode the one or more combined encodings with the decoder of the machine-learned synthesizer to generate predicted acoustic features. The system includes one or more modules configured to process the predicted acoustic features, the one or more modules comprising a machine-learned vocoder configured to generate a waveform of the expressive speech audio.

Generating expressive speech audio from text data

This specification describes a computer-implemented method of generating speech audio for use in a video game, wherein the speech audio is generated using a voice convertor that has been trained to convert audio data for a source speaker into audio data for a target speaker. The method comprises receiving: (i) source speech audio, and (ii) a target speaker identifier. The source speech audio comprises speech content in the voice of a source speaker. Source acoustic features are determined for the source speech audio. A target speaker embedding associated with the target speaker identifier is generated as output of a speaker encoder of the voice convertor. The target speaker embedding and the source acoustic features are inputted into an acoustic feature encoder of the voice convertor. One or more acoustic feature encodings are generated as output of the acoustic feature encoder. The one or more acoustic feature encodings are derived from the target speaker embedding and the source acoustic features. Target speech audio is generated for the target speaker. The target speech audio comprises the speech content in the voice of the target speaker. The generating comprises decoding the one or more acoustic feature encodings using an acoustic feature decoder of the voice convertor.

Speaker conversion for video games

Systems and methods are disclosed for enabling a player of a video game to designate custom voice utterances to control an in-game character. One or more machine learning models may learn in-game character actions associated with each of a number of player-defined utterances based on player demonstration of desired character actions. During execution of an instance of a video game, current game state information may be provided to the one or more trained machine learning models based on an indication that a given utterance was spoken by the player. A system may then cause one or more in-game actions to be performed by a non-player character in the instance of the video game based on output of the one or more machine learning models.

Custom voice control of video game character

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Redwood City, CA, United States of America

Dhaval Shah