Abstract: A method includes receiving a request indication indicating a GUI interaction by a user on a user device, and in response, providing to the device a response configured to cause the device to display, within a GUI, a structured prompt including a plurality of user input fields, each user input field representing a corresponding training sample and including a first corresponding text input field for capturing input text to be provided to an ML model, and a second corresponding text input field for capturing ground-truth output text. The method also includes receiving, from the device, the training samples, and, in response, adjusting the ML model using the training samples. The method further includes receiving, from the device, test input text, generating, using the adjusted ML model, test output text based on the test input text, and providing, to the user device, the test output text for display within the GUI.

Abstract: A computing device receives a first audio waveform representing a first utterance and a second utterance. The computing device receives identity data indicating that the first utterance corresponds to a first speaker and the second utterance corresponds to a second speaker. The computing device determines, based on the first utterance, the second utterance, and the identity data, a diarization model configured to distinguish between utterances by the first speaker and utterances by the second speaker. The computing device receives, exclusively of receiving further identity data indicating a source speaker of a third utterance, a second audio waveform representing the third utterance. The computing device determines, by way of the diarization model and independently of the further identity data of the first type, the source speaker of the third utterance. The computing device updates the diarization model based on the third utterance and the determined source speaker.

Abstract: A computing device receives a first audio waveform representing a first utterance and a second utterance. The computing device receives identity data indicating that the first utterance corresponds to a first speaker and the second utterance corresponds to a second speaker. The computing device determines, based on the first utterance, the second utterance, and the identity data, a diarization model configured to distinguish between utterances by the first speaker and utterances by the second speaker. The computing device receives, exclusively of receiving further identity data indicating a source speaker of a third utterance, a second audio waveform representing the third utterance. The computing device determines, by way of the diarization model and independently of the further identity data of the first type, the source speaker of the third utterance. The computing device updates the diarization model based on the third utterance and the determined source speaker.

Abstract: A computer-implemented method and associated computing device for translating speech can include receiving, at a microphone of a computing device, an audio signal representing speech of a user in a first language or in a second language at a first time. A positional relationship between the user and the computing device at the first time can be determined and utilized to determine whether the speech is in the first language or the second language. The method can further include obtaining, at the computing device, a machine translation of the speech represented by the audio signal based on the determined language, wherein the machine translation is: (i) in the second language when the determined language is the first language, or (ii) in the first language when the determined language is the second language. An audio representation of the machine translation can be output from a speaker of the computing device.

Abstract: A system provides a user interface through which users can flexibly tag individual items represented in an electronic catalog with user-defined tags, such as text strings, and obtain recommendations that are specific to particular tags. The tags and tag-item assignments created by each user are stored persistently in association with the user, and may be kept private to the user or exposed to others. Once a user has assigned a tag to a number of items, the user (or another user in some embodiments) can request and obtain recommendations that are specific to this tag. These recommendations may be generated in real time by a recommendation service that identifies items that are collectively similar or related to the items associated with the tag.

Abstract: A system provides a user interface through which users can flexibly tag individual items represented in an electronic catalog with user-defined tags, such as text strings, and obtain recommendations that are specific to particular tags. The tags and tag-item assignments created by each user are stored persistently in association with the user, and may be kept private to the user or exposed to others. Once a user has assigned a tag to a number of items, the user (or another user in some embodiments) can request and obtain recommendations that are specific to this tag. These recommendations may be generated in real time by a recommendation service that identifies items that are collectively similar or related to the items associated with the tag.

Abstract: A system provides a user interface through which users can flexibly tag individual items represented in an electronic catalog with user-defined tags, such as text strings, and obtain recommendations that are specific to particular tags. The tags and tag-item assignments created by each user are stored persistently in association with the user, and may be kept private to the user or exposed to others. Once a user has assigned a tag to a number of items, the user (or another user in some embodiments) can request and obtain recommendations that are specific to this tag. These recommendations may be generated in real time by a recommendation service that identifies items that are collectively similar or related to the items associated with the tag.

Abstract: A method and system for producing an item comparison may initially include obtaining data based on user activity that is indicative of an item and other items likely to be comparable to the item, and recording the data for later access. An item comparison may thereafter present an item of interest to the user along with other items identified based on the recorded data. Prior to producing the item comparison, the user may be provided at least one list of potentially comparable items that can be selected for the item comparison, the list including items in the recorded data that are likely to be comparable to the item of interest. The recorded data may be specific to the user or may include data obtained from other users. Data reflecting user activity with an item comparison may be recorded as additional data indicative of comparable items.