Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a group of records involves determining, based on one or more fields of metadata associated with the records, a plurality of candidate names, wherein each candidate name of the plurality of candidate names corresponds to semantic content of the one or more fields of a respective record of the group of records, for each candidate name, assigning a name relevance score based on respective word relevance scores assigned to respective words of the respective candidate name based on usage, selecting a candidate name in a manner that is influenced by the respective name relevance scores assigned to the respective candidate names and automatically assigning a name to the group of records using the candidate name.

A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.

The present disclosure relates to the development and design of bot interfaces and, more particularly, to one or more components, systems and methods of an intelligent development and design platform configured to assist users in the design, development and deployment of bot applications.

Examples described herein relate to portable playback devices, such as smart headphones and earbuds, and ultra-portable devices having built-in voice assistants. Some example techniques relate to user interaction with voice assistants. Further example techniques relate to voice guidance played back by the headphones to guide the user under certain conditions.

Disclosed is a server for supporting a communication environment between different electronic devices. The server includes a communication circuit, a memory, and a processor. The processor is electrically connected to the communication circuit and the memory. The processor is configured to receive a first voice signal transmitted from a second electronic device to a first electronic device through the communication circuit. The Processor is also configured to allow the first electronic device to transmit network connection information for connecting with the server to the second electronic device based on whether the first voice signal corresponds to a second voice signal stored in the memory.

A device, such as Network Microphone Device or a playback device, receives an indication of a track change associated with a playback queue output by a media playback system. In response, an input detection window is opened for a given time period. During the given time period the device is arranged to receive an input sound data stream representing sound detected by a microphone. The input sound data stream is analyzed for a plurality of command keywords and/or a wake-word for a Voice Assistant Service (VAS) and, based on the analysis, it is determined that the input sound data stream includes voice input data comprising a command keyword or a wake-word for a VAS. In response, the device takes appropriate action such as causing the media playback system to perform a command corresponding to the command keyword or sending at least part of the input sound data stream to the VAS.

An example system includes a processor to receive a summary of a conversation to be generated. The processor can input the summary into a trained summary-grounded conversation generator. The processor can receive a generated conversation from the trained summary-grounded conversation generator.

A device, such as Network Microphone Device or a playback device, receives an indication of a track change associated with a playback queue output by a media playback system. In response, an input detection window is opened for a given time period. During the given time period the device is arranged to receive an input sound data stream representing sound detected by a microphone. The input sound data stream is analyzed for a plurality of command keywords and/or a wake-word for a Voice Assistant Service (VAS) and, based on the analysis, it is determined that the input sound data stream includes voice input data comprising a command keyword or a wake-word for a VAS. In response, the device takes appropriate action such as causing the media playback system to perform a command corresponding to the command keyword or sending at least part of the input sound data stream to the VAS.

This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-adapted generative model that has been tuned using one or more task-specific seed examples. The method or technique can also include inputting dialog acts into the task-adapted generative model and obtaining synthetic utterances that are output by the task-adapted generative model. The method or technique can also include populating a synthetic training corpus with synthetic training examples that include the synthetic utterances. The synthetic training corpus may be suitable for training a natural language understanding model.

According to one embodiment, a recognition device includes storage and a processor. The storage is configured to store a first recognition model, a first data set, and tags, for each first recognition model. The processor is configured to acquire a second data set, execute recognition processing of the second recognition target data in the second data set by using the first recognition model, extract a significant tag of the tags stored in the storage in association with the first recognition model, based on the recognition processing result and the second correct data in the second data set, and create a second recognition model based on the acquired second data set and the first data set stored in the storage in association with the extracted tag.