Structures and protocols are presented for using or otherwise relating to a first mobile device (a smartphone or tablet computer or wearable device, e.g.) configured to be shared by two or more parties such that a subset of the parties may be addressed selectively (in content directed to such parties, e.g.) in a cost-effective manner.

A virtual meeting attendee includes a scheduling interface operatively coupled to a computerized meeting platform and configured to enable the virtual meeting attendee to be scheduled into meetings as a participant by users of the computerized meeting platform. The virtual meeting attendee also includes a content preparation machine configured, when the meeting attendee has been scheduled into a meeting as a participant, to receive meeting-associated content from computing devices of other participants scheduled into the meeting and output the meeting-associated content for display via a display device associated with the virtual meeting attendee.

An inbound call connection request may be received from a non-resident, directed to a controlled-environment facility resident and/or the resident's device. A determination may be made that a calling account of the resident does not have sufficient funds to pay for the inbound call, whereupon a message may be provided to the non-resident offering billing options, including at least a wireless carrier billing option, to complete the call connection. The call may be connected with the resident device in response to a determination a calling account of the inmate has sufficient funds to pay for the call or acceptance of one of the payment methods by the non-resident, along with authentication that the non-resident is associated with an address identifier (AID) of the resident device, and verification that the resident operating the device is associated with the AID of the device.

The present invention relates to a system for retrieving information from a network such as the Internet. A user creates a user-defined record in a database that identifies an information source, such as a web site, containing information of interest to the user. This record identifies the location of the information source and also contains a recognition grammar based upon a speech command assigned by the user. Upon receiving the speech command from the user that is described within the recognition grammar, a network interface system accesses the information source and retrieves the information requested by the user.

This disclosure describes techniques and systems for encoding instructions in audio data that, when output on a speaker of a first device in an environment, cause a second device to output content in the environment. In some instances, the audio data has a frequency that is inaudible to users in the environment. Thus, the first device is able to cause the second device to output the content without users in the environment hearing the instructions. In some instances, the first device also outputs content, and the content output by the second device is played at an offset relative to a position of the content output by the first device.

Systems for and methods of determining the priority of a call interaction include receiving a call interaction from a call center; validating, by a validation and transcription engine, that the call interaction is authentic; converting, by the validation and transcription engine, the call interaction into text; extracting, by a data calculation engine, organization, location, and time information from the text; calculating, by the data calculation engine, a priority of the call interaction from the extracted information and the text; determining that the call interaction should be transmitted to a queue of the call center for initial handling by a call center agent; and transmitting the call interaction, the calculated priority, and the extracted information to the call center.

Systems and methods for providing customized automatic speech recognition (ASR) in a customer support system are disclosed. In an example method, one or more data sources for training an ASR language model associated with the customer support system are identified, and one or more weighting models are selected, each weighting model applying a corresponding weight to each data source of the one or more data sources. The ASR language model is then trained based at least in part on the one or more data sources and the one or more weighting models, and a transcript may be generated for one or more customer support calls of the customer support system using the trained ASR language model.

Disclosed are methods and systems for voice phishing monitoring. For instance, a method includes receiving voice data of an incoming call to a communication device from an application associated with a user account and executing on the device, identifying an entity and interaction allegedly associated with the incoming call from the voice data, determining first fraud indicator data based on a number of the incoming call and second fraud indicator data based on a correspondence of user account interaction data to the entity and/or interaction, and providing the voice data to a trained machine learning system to receive third fraud indicator data based on content and/or a voice characteristic identified from the voice data. The method may further include determining a status for the incoming call of fraudulent or confirmed based on the first, second, and third fraud indicator data, and generating a notification indicating the status for display.

A computer-implemented method, system and computer program product for providing high quality speech recognition. A first speech-to-text model is selected to perform speech recognition of a customer's spoken words and a second speech-to-text model is selected to perform speech recognition of the agent's spoken words during a call. The combined results of the speech-to-text models used to process the customer's and agent's spoken words are then analyzed to generate a reference speech-to-text result. The customer speech data that was processed by the first speech-to-text model is reprocessed by multiple other speech-to-text models. A similarity analysis is performed on the results of these speech-to-text models with respect to the reference speech-to-text result resulting in similarity scores being assigned to these speech-to-text models. The speech-to-text model with the highest similarity score is then selected as the new speech-to-text model for performing speech recognition of the customer's spoken words during the call.

A method includes receiving data characterizing an utterance of a query associated with a tenant; providing, to an automated speech recognition engine, the received data and a profile selected from a plurality of profiles based on the tenant, the profile configuring the automated speech recognition engine to process the received data; receiving, from the automated speech recognition engine, a text string characterizing the query; and processing, via an ensemble of natural language agents configured based on the tenant, the text string characterizing the query to determine a textual response to the query, the textual response including at least one word from a first lexicon associated with the tenant. Related systems, methods, apparatus, and computer readable mediums are also described.