A dialogue system, a vehicle and a method for controlling the vehicle is disclosed. The method for controlling the vehicle includes: acquiring an utterance and a speech pattern by recognizing a speech when a speech of a plurality of speakers is input through a speech input device; classifying dialogue contents for each speaker based on the acquired utterance and speech pattern; acquiring a relationship between the speakers based on the acquired utterance; understanding an intention and a context for each speaker based on the acquired relationship between the speakers and the acquired dialogue content for each speaker determining an action corresponding to the acquired relationship and the acquired intention and context for each speaker, and outputting an utterance corresponding to the determined action; generating a control command corresponding to the determined action; and controlling a load based on the generated control command.

A network-based, e.g., cloud-based, background expert, is provided for predicting and/or accomplishing a user's goals. Moreover, the system can monitor, in the background, user generated data and/or publicly available data to determine and/or infer a user's goal, with or without an active indication/request from the user. Typically, the user-generated data can include user conversations, such as, but not limited to, speech data in a voice call, text messages, chat dialogues, etc. Further, the system can identify an action or task that facilitates accomplishment of the user goal in real-time. Moreover, the system can automatically perform the action/task and/or request user authorization prior to performing the action/task.

Among other things, requests are received from voice assistant devices expressed in accordance with different corresponding protocols of one or more voice assistant frameworks. Each of the requests represents a voiced input by a user to the corresponding voice assistant device. The received requests are re-expressed in accordance with a common request protocol. Based on the received requests, responses to the requests are expressed in accordance with a common response protocol. Each of the responses is re-expressed according to a protocol of the framework with respect to which the corresponding request was expressed. The responses are sent to the voice assistant devices for presentation to the users.

Disclosed is an artificial intelligence voice recognition apparatus including: a microphone; a controller configured to process a voice input received through the microphone; and a display configured to display information corresponding to each stage of a procedure of processing the voice input. Accordingly, it is possible to provide a user with diverse information according to a voice recognition process.

Techniques are disclosed for adjusting a ranking of information content of a software application based on feedback from a user. One embodiment presented herein includes a method comprising receiving, at a computing device, an audio stream comprising audio of the user, the audio being indicative of feedback related to information content. The method further comprises analyzing the audio stream for paralinguistic information to determine an attribute of the user. The method further comprises adjusting a ranking of the information content based on at least one of the feedback and additional feedback and the determined attribute of the user.

Systems, methods, and computer-readable media are disclosed for dynamic voice search transitioning. Example methods may include receiving, by a computer system in communication with a display, a first incoming voice data indication, initiating a first user interface theme for presentation at a display, wherein the first user interface theme is a default user interface theme, and receiving first voice data. Example methods may include sending the first voice data to a remote server for processing, receiving an indication from the remote server to initiate a second user interface theme, and initiating the second user interface theme for presentation at the display.

In non-limiting examples of the present disclosure, systems, methods and devices for executing a command by a digital assistant in a group device environment are presented. A plurality of devices with digital assistants may be clustered for the duration of an event. One of the devices of the cluster may be assigned as an arbitrator device for the cluster. A user may issue a verbal command executable by a digital assistant of the cluster. The user that issued the verbal command may be identified via voice analysis. A determination may be made as to whether the verbal command corresponds to an intent to share content with a plurality of members of the cluster, or a specific member of the cluster, and a device of the cluster may be selected for executing a reply to the verbal command based on the determined intent and the executing device's presentation capabilities.

In non-limiting examples of the present disclosure, systems, methods and devices for executing a command by a digital assistant in a group device environment are presented. A plurality of devices with digital assistants may be clustered for the duration of an event. One of the devices of the cluster may be assigned as an arbitrator device for the cluster. A user may issue a verbal command executable by a digital assistant of the cluster. The user that issued the verbal command may be identified via voice analysis. A determination may be made as to whether the verbal command corresponds to an intent to share content with a plurality of members of the cluster, or a specific member of the cluster, and a device of the cluster may be selected for executing a reply to the verbal command based on the determined intent and the executing device's presentation capabilities.

Among other things, a developer of an interaction application for an enterprise can create items of content to be provided to an assistant platform for use in responses to requests of end-users. The developer can deploy the interaction application using defined items of content and an available general interaction model including intents and sample utterances having slots. The developer can deploy the interaction application without requiring the developer to formulate any of the intents, sample utterances, or slots of the general interaction model.

A local extremum calculator detects a local maximum sample and a local minimum sample of a digital audio signal. A number-of-sample detector detects a sample interval between the local maximum sample and the local minimum sample. A difference value calculator calculates difference values between adjacent samples. A correction value calculator calculates a first correction value by multiplying the difference value between the local maximum sample and a first adjacent sample by a coefficient and calculates a second correction value by multiplying the difference value between the local minimum sample and a second adjacent sample by the coefficient. When a periodic signal detector detects that the digital audio signal is a single sine wave, an adder/subtractor does not add the first correction value to the first adjacent sample, and does not subtract the second correction value from the second adjacent sample.