Disclosed are a method and a system of recommending an accommodation for tourists using multi-criteria decision making (MCDM) and augmented reality. A method of recommending an accommodation for tourists using multi-criteria decision making and augmented reality, which is performed by a server device includes: selecting a recommendation target accommodation based on a current location of a user; selecting a plurality of recommended accommodations by MCDM based on user information including pre-registered preference information among the recommendation target accommodations; and providing an augmented reality interface displaying information on a recommended accommodation to a user terminal.

Exemplary embodiments relate to adapting a generic language model during runtime using domain-specific language model data. The system performs an audio frame-level analysis, to determine if the utterance corresponds to a particular domain and whether the ASR hypothesis needs to be rescored. The system processes, using a trained classifier, the ASR hypothesis (a partial hypothesis) generated for the audio data processed so far. The system determines whether to rescore the hypothesis after every few audio frames (representing a word in the utterance) are processed by the speech recognition system.

A method of recognizing speech commands includes generating a background acoustic model for a sound using a first sound sample, the background acoustic model characterized by a first precision metric. A foreground acoustic model is generated for the sound using a second sound sample, the foreground acoustic model characterized by a second precision metric. A third sound sample is received and decoded by assigning a weight to the third sound sample corresponding to a probability that the sound sample originated in a foreground using the foreground acoustic model and the background acoustic model. The method further includes determining if the weight meets predefined criteria for assigning the third sound sample to the foreground and, when the weight meets the predefined criteria, interpreting the third sound sample as a portion of a speech command. Otherwise, recognition of the third sound sample as a portion of a speech command is forgone.

A method for voice recognition (VR)-based task allocation and hotword detection function control for within a wireless network having a hands-free (HF) node, e.g., a motor vehicle or telematics unit thereof, and an audio gateway (AG) node such as a wireless device, includes detecting, via a first wireless chipset of the HF node, a second wireless chipset of the AG node. The wireless chipsets include respective VR engines responsive to a corresponding hotword. The method includes establishing a Bluetooth or other wireless connection between the wireless chipsets in response to detecting the second wireless chipset. The method may include automatically transmitting a disable command signal to the second wireless chipset, via the first wireless chipset, to thereby disable a hotword detection function of the second wireless chipset. The method may be recorded on a computer readable medium as instructions executable by a processor.

The present invention provides a method for updating speech recognition system through air. Client ASR servers connect with a central ASR cloud server through Internet. New version of ASR system is stored in the central ASR cloud server for being selected and downloaded by the client ASR servers for using.

Particular embodiments described herein provide for an electronic device that can be configured to receive a verbal command to active a device with an unknown label, derive a probable device and a label for the probable device, activate the probable device, determine that the activated probable device is the same device to be activated by the verbal command, and store the label and a description for the device. In some examples, the label is associated with the description.

Systems and methods to briefly deviate from and resume back to amending a section of a note are disclosed. Exemplary implementations may: obtain audio information representing sound captured by an audio section of a client computing platform, such sound including speech from a user associated with the client computing platform; effectuate presentation of a graphical user interface that includes sections of the note; analyze the audio information to determine which individual ones of the spoken inputs are the primary spoken input or the deviant spoken input; determine, based on analysis, which section of the note to which the deviant spoken input is related; alternately amend, based on the determination, sections of the note by deviating from one section to another section and returning back to the one section for continued population; and effectuate, via the user interface, presentation of the alternating amendments to the sections of the note.

A portable terminal includes: a storage part which holds work information in which a plurality of items included in work are associated with respective work results; a speech recognition part which stores a work result(s) obtained by recognizing an utterance(s) of a worker in the storage part; and a communication part which transmits, when the speech recognition part recognizes a predetermined utterance(s) of a worker, a work result(s) held by the storage part to a management server or another (other) portable terminal(s). When the communication part receives a work result(s) from the management server or another (other) portable terminal(s), the communication part stores the received work result(s) in the storage part.

An in-vehicle terminal sends a spoken voice as a voice signal to a relay server, and the relay server includes a voice recognition unit which converts the received voice signal into a string, and a control unit which searches for information stored in a main database or a temporary storage database by using the string and sends a search result to the in-vehicle terminal, and, upon searching for information stored in the main database, stores the search result in the temporary storage database. Upon receiving a voice signal, when the search result is stored in the temporary storage database, the control unit searches for information stored in the temporary storage database by using the string converted from the received voice signal, and, when the search result is not stored in the temporary storage database, the control unit searches for information stored in the main database by using the string.

In some cases, lower quality, large scale training data can be automatically generated by automatic labeling. The generated training data can be used to pre-train a machine learning model. For instance, the model can be a model for detection of verbal harassment. Parameters of the pre-trained model can be refined or updated using another one or more higher-quality sets of training data, with which the model can be subsequently trained.