Abstract: Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving an indication of a priority level of an interworking function used by a boundary clock node; and selecting a best master clock using an algorithm that considers the priority level of the boundary clock node. Other embodiments are disclosed.

Abstract: This disclosure describes a framework for generating audio translations (e.g., dubbing) of videos, including being performed locally on a client device. For instance, this disclosure describes a video dubbing system that utilizes length-aware speech translation models to provide dynamic audio translations for videos that accurately align with the source audio. In particular, the video dubbing system utilizes length-aware translations to prevent audio misalignment of translated audio, resulting in natural-sounding audio translations. Additionally, the video dubbing system uses techniques such as beam search to efficiently determine dynamic translated audio from multiple versions that align accurately with the source audio. As further described below, the video dubbing system seamlessly provides translated audio phrases in real time that dynamically add or remove words to match the duration of the source audio phrases, resulting in a much more natural dubbing experience.

Abstract: This disclosure describes a framework for generating real-time audio translations of videos on a client device. Specifically, this disclosure describes a video dubbing system that utilizes a concurrent batch-processing architecture to provide real-time audio translations of videos on a client device. Additionally, in one or more implementations, the video dubbing system utilizes time-aware segmentation to prevent audio misalignment of the translated audio. As described below, the video dubbing system efficiently provides high-quality audio translations of videos that accurately align with the video content for the entire video, regardless of the video's length.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved recognition of multiple languages in audio data. One method including: receiving a trained split head multilingual neural network model, the trained split head multilingual neural network model including shared acoustic model layers and a plurality of projection layers, each projection layer of the plurality of projection layers corresponding to a language that the trained split head multilingual neural network model recognizes; receiving audio data, the audio data including speech in a plurality of languages in the audio data, the speech in the plurality of languages corresponding the language recognized by a projection layer of the plurality of projection layers of the trained split head multilingual neural network model; and classifying one or more languages of the speech of the audio data using the trained split head multilingual neural network model.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved recognition of multiple languages in audio data. One method including: receiving a trained split head multilingual neural network model, the trained split head multilingual neural network model including shared acoustic model layers and a plurality of projection layers, each projection layer of the plurality of projection layers corresponding to a language that the trained split head multilingual neural network model recognizes; receiving audio data, the audio data including speech in a plurality of languages in the audio data, the speech in the plurality of languages corresponding the language recognized by a projection layer of the plurality of projection layers of the trained split head multilingual neural network model; and classifying one or more languages of the speech of the audio data using the trained split head multilingual neural network model.

Abstract: A technique translates speech in a first language to text in a second language manner in a manner that is appropriate for the gender of the speaker. In some implementations, the technique receives an input setting that specifies one of three translation modes: masculine mode, feminine mode, and auto mode. The first two modes produce translations in masculine and feminine modes by default, respectively, while the auto mode produces translations in forms that are based on the detected characteristics of audio signals. According to some implementations, the technique uses a training framework that automatically converts a corpus of training examples that exhibit gender bias (e.g., a male gender bias) to training examples having a reducing incidence of gender bias. In some implementations, the training framework updates weights of the machine-trained model based on a combination of two loss components: translation loss and gender loss.

Abstract: Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving an indication of a priority level of an interworking function used by a boundary clock node; and selecting a best master clock using an algorithm that considers the priority level of the boundary clock node. Other embodiments are disclosed.

Abstract: Methods, systems, and computer storage media for providing speech synthesis using a code-mixed speech engine in a speech synthesis system. A code-mixed speech engine supports generating natural and intelligible speech in a target speaker voice—for code-mixed-text of two or more languages—based on a code-mixed speech model that supports both code-mixing and cross-locale voice transfer scenarios. In operation, code-mixed training data associated with a plurality of different languages is accessed. A code-mixed speech model—associated with a training engine and an inference engine that support generating code-mixed synthesized speech—is generated. The code-mixed speech model is deployed. A request being received for synthesized speech of a speech synthesis service. An instance of code-mixed synthesized speech is generated. The instance of code-mixed synthesized speech is generated using the code-mixed speech model.

Abstract: An automatic speech recognition (ASR) system recognizes speech expressed in different languages. The ASR system includes a language-agnostic encoding component and prediction component. A language-specific joint analysis system generates first-language probabilities for symbols of a first language and second-language probabilities for symbols of a second language, based on outputs generated by the encoding component and the prediction component. The ASR system then modifies the probabilities produced by the joint analysis system by language-specific weighting information that, in turn, is produced by an attention system. This yields modified first-language probabilities and modified second-language probabilities. Finally, the ASR system predicts an updated instance of label information based on the modified first-language probabilities and the modified second-language probabilities. The ASR system can be successfully applied to recognize an utterance that combines words in two or more languages.

Abstract: Systems, methods, and computer-readable storage devices are disclosed for improved recognition of multiple languages in audio data. One method including: receiving a trained split head multilingual neural network model, the trained split head multilingual neural network model including shared acoustic model layers and a plurality of projection layers, each projection layer of the plurality of projection layers corresponding to a language that the trained split head multilingual neural network model recognizes; receiving audio data, the audio data including speech in a plurality of languages in the audio data, the speech in the plurality of languages corresponding the language recognized by a projection layer of the plurality of projection layers of the trained split head multilingual neural network model; and classifying one or more languages of the speech of the audio data using the trained split head multilingual neural network model.

Abstract: An automatic speech recognition (ASR) system recognizes speech expressed in different languages. The ASR system includes a language-agnostic encoding component and prediction component. A language-specific joint analysis system generates first-language probabilities for symbols of a first language and second-language probabilities for symbols of a second language, based on outputs generated by the encoding component and the prediction component. The ASR system then modifies the probabilities produced by the joint analysis system by language-specific weighting information that, in turn, is produced by an attention system. This yields modified first-language probabilities and modified second-language probabilities. Finally, the ASR system predicts an updated instance of label information based on the modified first-language probabilities and the modified second-language probabilities. The ASR system can be successfully applied to recognize an utterance that combines words in two or more languages.

Abstract: One embodiment provides a method, including: obtaining a first question on a multiple choice exam comprising a plurality of possible answers; receiving his or her answer selection of one of the plurality of possible answers; identifying an answer time corresponding to the amount of time between presentation of the question and receiving the answer selection; determining if the test taker is guessing the answer to the question, wherein the determining comprises (i) comparing the answer time to a predetermined threshold answer time and (ii) ascertaining that the test taker is guessing when the answer time is outside the threshold answer time; providing to the test taker one or more additional questions, wherein the one or more additional questions are related to the first question; and evaluating the test taker using at least one of: the answer selection and any test taker response to the one or more additional questions.

Abstract: One embodiment provides a method, including utilizing at least one processor to execute computer code that performs the steps of: receiving, from a user, one or more keywords, wherein the one or more keywords identify, within a social media data set, a subset of social media data; determining, based on social media statistical information, an estimated volume of the subset of the social media data; determining, based on the social media statistical information, an estimated relevance of the subset of social media data to the user; and updating the one or more keywords, to improve the estimated volume and estimated relevance. Other aspects are described and claimed.

Abstract: Methods, systems, and computer program products for determining a personalized advertisement channel are provided herein. A computer-implemented method includes processing multiple data sources associated with a user based on data derived from multiple notification messages accessed via an operating system feature of a mobile device of the user; determining a current state in connection with a product and/or service based on said processing, wherein said current state comprises a current likelihood that the user will purchase the product and/or service; computing probabilities, conditioned on each of multiple advertisement media, that the user will transition from a first state to a second state based on said processing; and identifying one of the advertisement media to implement with the product and/or service to transition the user from the current state to a separate user state, wherein said identifying is based on (i) the current state and (ii) the multiple computed probabilities.

Abstract: One embodiment a method for ensuring group cohesiveness, the method including: utilizing at least one processor to execute computer code that performs the steps of: obtaining community information associated with a group; generating, based on the community information, a group profile; detecting a user action; determining, based on the group profile, if the user action negatively affects group cohesiveness; and generating, based on the determining, a notification. Other aspects are described and claimed.

Abstract: One embodiment provides a method, the method including: utilizing at least one processor to execute computer code that performs the steps of: identifying one or more access permissions of an application associated with an electronic device; identifying, based on the access permissions, information accessible to the application; analyzing user information within with the accessible information; determining, based on the analyzing, at least one personal exposure risk; and providing to a user, based on the at least one information exposure risk, at least one proposed solution. Other aspects are described and claimed.

Abstract: One embodiment provides a method, including: obtaining a first question on a multiple choice exam comprising a plurality of possible answers; receiving his or her answer selection of one of the plurality of possible answers; identifying an answer time corresponding to the amount of time between presentation of the question and receiving the answer selection; determining if the test taker is guessing the answer to the question, wherein the determining comprises (i) comparing the answer time to a predetermined threshold answer time and (ii) ascertaining that the test taker is guessing when the answer time is outside the threshold answer time; providing to the test taker one or more additional questions, wherein the one or more additional questions are related to the first question; and evaluating the test taker using at least one of: the answer selection and any test taker response to the one or more additional questions.

Abstract: One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: receiving motion sensor information from a plurality of sensors on a wearable device; identifying, based on the motion sensor information, a motion pattern corresponding to an activity of a user; comparing the motion pattern to a plurality of stored motion patterns; determining, based on the comparing, if the motion pattern matches one of the stored motion patterns that is identified as a motion pattern of a sensitive activity; and modifying, whether the motion pattern matches one of the stored motion pattern identified as a motion pattern of a sensitive activity. Other aspects are described and claimed.

Abstract: Methods and arrangements for rendering social proximity networks for mobile phones. A contemplated method includes: obtaining communication data for a mobile phone; deriving from the communication data a social proximity score with respect to at least one individual associated with at least one device that has communicated with the mobile phone; and storing the social proximity score. Other variants and embodiments are broadly contemplated herein.

Abstract: One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: receiving motion sensor information from a plurality of sensors on a wearable device; identifying, based on the motion sensor information, a motion pattern corresponding to an activity of a user; comparing the motion pattern to a plurality of stored motion patterns; determining, based on the comparing, if the motion pattern matches one of the stored motion patterns that is identified as a motion pattern of a sensitive activity; and modifying, whether the motion pattern matches one of the stored motion pattern identified as a motion pattern of a sensitive activity. Other aspects are described and claimed.