A natural language processing system may use system response configuration data to determine customized output data forms when outputting data for a user. The system response configuration data may represent various output attributes the system may use when creating output data. The system may have a certain number of existing profiles where a profile is associated with certain settings for the system response configuration data/attributes. The system may also use various data such as context data, sentiment data, or the like to customize system response configuration data during a dialog. Other components, such as natural language generation (NLG), text-to-speech (TTS), or the like, may use the customized system response configuration data to determine the form, timing, etc. of output data to be presented to a user.

A natural language processing system may use system response configuration data to determine customized output data forms when outputting data for a user. The system response configuration data may represent various output attributes the system may use when creating output data. The system may have a certain number of existing profiles where a profile is associated with certain settings for the system response configuration data/attributes. The system may also use various data such as context data, sentiment data, or the like to customize system response configuration data during a dialog. Other components, such as natural language generation (NLG), text-to-speech (TTS), or the like, may use the customized system response configuration data to determine the form, timing, etc. of output data to be presented to a user.

A system for coordinating reactions of a virtual character with script spoken by a player in a video game or presentation, comprising an internet-connected server executing software and streaming video games or presentations to a player's computerized device. The system senses start of a dialogue between the player and the virtual character, displays a script for the player on a display of the computerized platform, prompts the player to speak the script. A timer then starts, or the system tracks an audio stream of the spoken script, determines where the player is in the script by the timer or the audio stream, and causes specific actions and responses of the virtual character according to pre-programmed association of actions and responses of the character to points of time or specific variations in the audio stream.

A system for coordinating reactions of a virtual character with script spoken by a player in a video game or presentation, comprising an internet-connected server executing software and streaming video games or presentations to a player's computerized device. The system senses start of a dialogue between the player and the virtual character, displays a script for the player on a display of the computerized platform, prompts the player to speak the script. A timer then starts, or the system tracks an audio stream of the spoken script, determines where the player is in the script by the timer or the audio stream, and causes specific actions and responses of the virtual character according to pre-programmed association of actions and responses of the character to points of time or specific variations in the audio stream.

Techniques for performing text-to-speech are described. An exemplary method includes receiving a request to generate audio from input text; generating audio from the input text by: generating a first number of vectors from phoneme embeddings representing the input text, predicting one or more spectrograms having the first number of frames using multiple scales wherein a coarser scale influences a finer scale, concatenating the first number of vectors and the predicted one or more spectrograms, generating at least one mel spectrogram from the concatenated vectors and the predicted one or more spectrograms, and converting, with a vocoder, the at least one mel spectrogram frames to audio; and outputting the generated audio according to the request.

Techniques regarding speech characterization are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a speech analysis component that can determine a condition of an origin of an audio signal based on a difference between a first feature of the audio signal and a second feature of a synthesized reference audio signal.

Techniques regarding speech characterization are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a speech analysis component that can determine a condition of an origin of an audio signal based on a difference between a first feature of the audio signal and a second feature of a synthesized reference audio signal.

A method for removing private data from an acoustic model includes capturing speech from a large population of users, creating a text-to-speech voice from at least a portion of the large population of users, discarding speech data from a database of speech, creating text-to-speech waveforms from the text-to-speech voice and the new database of speech with the discarded speech data and generating an automatic speech recognition model using the text-to-speech waveforms.

A method for removing private data from an acoustic model includes capturing speech from a large population of users, creating a text-to-speech voice from at least a portion of the large population of users, discarding speech data from a database of speech, creating text-to-speech waveforms from the text-to-speech voice and the new database of speech with the discarded speech data and generating an automatic speech recognition model using the text-to-speech waveforms.

Techniques for the generation of dubbed audio for an audio/video are described. An exemplary approach is to receive a request to generate dubbed speech for an audio/visual file; and in response to the request to: extract speech segments from an audio track of the audio/visual file associated with identified speakers; translate the extracted speech segments into a target language; determine a machine learning model per identified speaker, the trained machine learning models to be used to generate a spoken version of the translated, extracted speech segments based on the identified speaker; generate, per translated, extracted speech segment, a spoken version of the translated, extracted speech segments using a trained machine learning model that corresponds to the identified speaker of the translated, extracted speech segment and prosody information for the extracted speech segments; and replace the extracted speech segments from the audio track of the audio/visual file with the spoken versions spoken version of the translated, extracted speech segments to generate a modified audio track.