Abstract: The performance of a generative agent (GA) is improved using live interactions. A conversation between a user and the GA is monitored while the GA produces responses from one or more pre-trained models. For each GA response, the system computes a quality score and compares it to a predefined threshold. When the score fails to satisfy the threshold, the response is automatically flagged, the session is paused, and the flagged response is withheld from presentation to the user. An intervention interface is then presented that enables a human agent to modify the flagged response.

Abstract: The extraction of key values from a conversation may be facilitated by using automation techniques. Communications of a conversation may be processed to determine a natural language intent, and the intent may be used to determine one or more target keys. The automated process may be used to obtain values for the one or more target keys. For a target key, a prompt may be presented to a user in the conversation, a response may be received from the user, and the response may be processed with a value extractor corresponding to the target key to determine a value for the target key. The values may then be presented to user in the conversation or used for other processing.

Abstract: A diffusion model may be used to generate an audio signal from text. The diffusion model may process received text and noise vectors to compute encoded audio vectors that correspond to the text. The encoded audio vectors may be decoded to generate an audio signal of a person speaking the text that may be presented to a user. The diffusion model may process a sequence of byte-encoding vectors corresponding to the text, and the use of the byte-encoding vectors may allow for the generation of higher quality audio signals. In some implementations, prompt audio of a person may also be used to generate an audio signal that resembles the speech of that person.

Abstract: A diffusion model may be used to generate an audio signal from text. The diffusion model may process received text and noise vectors to compute encoded audio vectors that correspond to the text. The encoded audio vectors may be decoded to generate an audio signal of a person speaking the text that may be presented to a user. The diffusion model may process a sequence of byte-encoding vectors corresponding to the text, and the use of the byte-encoding vectors may allow for the generation of higher quality audio signals. In some implementations, prompt audio of a person may also be used to generate an audio signal that resembles the speech of that person.

Abstract: The extraction of key values from a conversation may be facilitated by using automation techniques. Communications of a conversation may be processed to determine a natural language intent, and the intent may be used to determine one or more target keys. The automated process may be used to obtain values for the one or more target keys. For a target key, a prompt may be presented to a user in the conversation, a response may be received from the user, and the response may be processed with a value extractor corresponding to the target key to determine a value for the target key. The values may then be presented to user in the conversation or used for other processing.