Abstract: An architecture related to the creation of, and attribution to, experiences based on location, popularity, captured media, and/or objects. A method can comprise receiving social media data associated with a user associated with a user identity, generating an attributable identity token based on the social media data and a learning model, wherein the attributable identity token is associated with the user identity and is representative of location data and experience data, and wherein the learning model comprises a collection of rules, and disseminating, by the device, the attributable identity token to a group of user identities associated with a social media operator entity.

Abstract: Tasks associated with users can be managed for efficient workflow management. A task management component (TMC) can analyze, including performing artificial intelligence-based analysis on, task-related information relating to associated with a user(s), assessment information relating to assessing performance or expertise associated with a task, biometric information relating to health, diet, and activity associated with the user(s), and/or user(s) feedback information. Based on the analysis, TMC can adaptively adjust respective attributes associated with respective tasks, resulting in respective adjusted attributes associated with the respective tasks. Based on the respective adjusted attributes, TMC can determine task information and can present the task information to a device(s) associated with the user(s) to facilitate performance of the tasks.

Abstract: A battery cell includes a housing, an electrode assembly accommodated in the housing, and an end cover configured to buckle to an opening of the housing. The end cover comprises a first body part, a first end wall, and a first peripheral wall. The first body part is configured to be connected to the housing. The first end wall is farther away from the electrode assembly than the first body part. The first peripheral wall is disposed around an edge of the first end wall and connected to the first body part, and the first peripheral wall and the first end wall jointly form a first accommodation space used to accommodate at least a part of a tab of the electrode assembly. The first peripheral wall comprises a first arc wall extending from the first end wall to the first body part along a first arc trajectory.

Abstract: A clamping triaxial seepage and acoustic coupling rock tensile testing machine includes a sample and a scaffold-type tensile testing device. The scaffold-type tensile testing device has an upper chuck and a lower chuck. The upper chuck has an acoustic transmitting channel, one end of which communicating with the outside, and the other end of which having an acoustic transmitting probe. The lower chuck has an acoustic receiving channel, one end of which communicating with the outside, and the other end having acoustic receiving probe. An upper end face of the sample has with a seepage outflow hole while the upper chuck has a seepage outflow channel connected with the seepage outflow hole. A lower end face of the sample has a seepage inflow hole while the lower chuck has a seepage entry channel is connected with the seepage inflow hole.

Abstract: A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.

Abstract: A mobile device is described which is adapted for automatic speech recognition (ASR). A speech input receives an unknown speech input signal from a user. A local controller determines if a remote ASR processing condition is met, transforms the speech input signal into a selected one of multiple different speech representation types, and sends the transformed speech input signal to a remote server for remote ASR processing. A local ASR arrangement performs local ASR processing of the speech input including processing any speech recognition results received from the remote server.

Abstract: A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.

Abstract: In many speech-enabled applications, adaptation of speech recognition and language understanding tools for different users are employed. With such adaptation, identifying the particular user precedes applying the speech recognition and language understanding tools. According to at least one example embodiment, a method and corresponding apparatus of identifying a user includes comparing personal information data received from a user network device against personal information accessible by the server; and identifying a speech profile specific to the user based on the results of comparing the personal information data retrieved from the first user network device against the personal information accessible by the server. The identified speech profile is used in processing a speech of the user. Through use of the method or corresponding apparatus, a user can proceed directly to the use of the speech recognition or other applications and bypassing a login sequence.

Abstract: A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.

Abstract: A method for speech recognition is implemented in the specific form of computer processes that function in a computer processor. That is, one or more computer processes: process a speech input to produce a sequence of representative speech vectors and perform multiple recognition passes to determine a recognition output corresponding to the speech input. At least one generic recognition pass is based on a generic speech recognition arrangement using generic modeling of a broad general class of input speech. And at least one adapted recognition pass is based on a speech adapted arrangement using pre-adapted modeling of a specific sub-class of the general class of input speech.

Abstract: A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.

Abstract: A mobile device is described which is adapted for automatic speech recognition (ASR). A speech input receives an unknown speech input signal from a user. A local controller determines if a remote ASR processing condition is met, transforms the speech input signal into a selected one of multiple different speech representation types, and sends the transformed speech input signal to a remote server for remote ASR processing. A local ASR arrangement performs local ASR processing of the speech input including processing any speech recognition results received from the remote server.

Abstract: In many speech-enabled applications, adaptation of speech recognition and language understanding tools for different users are employed. With such adaptation, identifying the particular user precedes applying the speech recognition and language understanding tools. According to at least one example embodiment, a method and corresponding apparatus of identifying a user includes comparing personal information data received from a user network device against personal information accessible by the server; and identifying a speech profile specific to the user based on the results of comparing the personal information data retrieved from the first user network device against the personal information accessible by the server. The identified speech profile is used in processing a speech of the user. Through use of the method or corresponding apparatus, a user can proceed directly to the use of the speech recognition or other applications and bypassing a login sequence.

Abstract: A method for speech recognition is implemented in the specific form of computer processes that function in a computer processor. That is, one or more computer processes: process a speech input to produce a sequence of representative speech vectors and perform multiple recognition passes to determine a recognition output corresponding to the speech input. At least one generic recognition pass is based on a generic speech recognition arrangement using generic modeling of a broad general class of input speech. And at least one adapted recognition pass is based on a speech adapted arrangement using pre-adapted modeling of a specific sub-class of the general class of input speech.

Abstract: Methods are given for improving discriminative training of hidden Markov models for continuous speech recognition. For a mixture component of a hidden Markov model state, a gradient adjustment is calculated of the standard deviation of the mixture component. If the calculated gradient adjustment is greater than a first threshold amount, an adjustment is performed of the standard deviation of the mixture component using the first threshold. If the calculated gradient adjustment is less than a second threshold amount, an adjustment is performed of the standard deviation of the mixture component using the second threshold. Otherwise, an adjustment is performed of the standard deviation of the mixture component using the calculated gradient adjustment.