Abstract: A method includes obtaining, by an electronic device, an audio segment comprising one or more audio events of a target subject. The method also includes extracting, by the electronic device, audio embeddings from the one or more audio events using an embedding model, the embedding model comprising a trained machine learning model. The method further includes comparing, by the electronic device, the extracted audio embeddings with a match profile of the target subject, the match profile generated during an enrollment stage. The method also includes generating, by the electronic device, a label for the audio segment based on whether or not the extracted audio embeddings match the match profile, wherein the label enables correlation of the audio segment with the target subject for monitoring a health condition of the target subject.

Abstract: A method is provided for extracting machine readable data structures from unstructured, low-resource language input text. The method includes obtaining a corpus of high-resource language data structures, filtering the corpus of high-resource language data structures to obtain a filtered corpus of high-resource language data structures, obtaining entity types for each entity of each filtered high-resource language data structure, performing type substitution for each obtained entity by replacing each entity with an entity of the same type to generate type substituted data structures, and replacing each entity with an equivalent a corresponding low-resource language data structure entity to generate code switched sentences. The method further includes generating an augmented data structure corpus, training a multi-head self-attention transformer model, and providing the unstructured low-resource language input text to the trained model to extract the machine readable data structures.

Abstract: One embodiment provides a method, including: detecting, at an input device of an information handling device, voice input; determining, using a processor, whether the voice input corresponds to a voice command; identifying, responsive to determining that the voice input corresponds to a voice command, that the voice command is associated with an enabled voice command; determining, using a processor, whether a characteristic of the voice command corresponds to a predetermined input characteristic; and performing, responsive to identifying that the voice command is associated with the enabled voice command and responsive to determining that the characteristic corresponds to the predetermined input characteristic, an action corresponding to the enabled voice command. Other aspects are described and claimed.

Abstract: Disclosed embodiments relate to aligning pairs of sentences. Techniques can include receiving a plurality of sentences; generating a graph for each of at least two sentences of the plurality of sentences, wherein generating a graph for each sentence of the at least two sentences comprises: identifying one or more tokens for the sentence; and connecting via edges the one or more tokens; generating a combined graph for the at least two sentences wherein generating a combined graph comprises: aligning the identified tokens of the at least two sentences of the plurality of sentences; identifying matching and non-matching tokens between the at least two sentences based on the alignment; and merging matching tokens into a combined graph node.

Abstract: An adaptive virtual assistant system can be configured to change an attribute of a virtual assistant based on user responses, environmental conditions, and/or topics of discussion. For example, the virtual assistant system can determine, based at least in part on user data, a communication profile that is associated with the virtual assistant and determine first communication data comprising a first communication attribute based on the communication profile. In some instances, the system can transmit the first communication data to a user device and receive, from the user device, input audio data representing a user utterance. Based at least in part on the input audio data, the system can determine second communication data comprising a second communication attribute and transmit the second communication data to the user device.

Abstract: A method and an apparatus for recognizing a voice are provided. The method may include: inputting a target voice into a pre-trained voice recognition model to obtain an initial text output by at least one recognition network in the voice recognition model, the recognition network including a plurality of preset types of processing layers, and at least one type of processing layer of the recognition network being obtained by training based on a voice sample in a preset direction interval; and determining a voice recognition result of the target voice, based on the initial text.

Abstract: A method for analyzing an electronic document including structured data and unstructured data may include applying a machine learning model to determine whether one or more rows of the electronic document correspond to a header row. The machine learning model may be trained to determine whether one or more cells of a row corresponds to a header field by determining whether a text value included in the cells corresponds to an entity. A row may be identified as a header row based on an output of the machine learning model indicating more than a threshold quantity of cells included in the row correspond to a header field. At least a portion of the structured data included in the electronic document may be extracted based on the entity included in the cells of the row identified as the header row. Related systems and computer program products are also provided.

Abstract: An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.

Abstract: This application describes methods and apparatus for speaker recognition. An apparatus according to an embodiment has an analyzer for analyzing each frame of a sequence of frames of audio data which correspond to speech sounds uttered by a user to determine at least one characteristic of the speech sound of that frame. An assessment module determines, for each frame of audio data, a contribution indicator of the extent to which that frame of audio data should be used for speaker recognition processing based on the determined characteristic of the speech sound. Said contribution indicator comprises a weighting to be applied to each frame in the speaker recognition processing. In this way frames which correspond to speech sounds that are of most use for speaker discrimination may be emphasized and/or frames which correspond to speech sounds that are of least use for speaker discrimination may be de-emphasized.

Abstract: A system and method of keyword spotting using two neural networks is disclosed. The system is in sleep mode most of the time, and wakes up periodically. Upon waking, a limited duration of audio is examined. This may be performed using an auxiliary neural network. If any audio activity is detected in this duration, the system fully wakes and examines a longer duration of audio for keywords. The keyword spotting is also performed by the main neural network, which may be a convolutional neural network (CNN).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing residual quantization are described. One example of the methods includes performing a first residual quantization on a first target residual signal at a first bit rate to generate a first quantized residual signal. A second target residual signal is generated based at least on the first quantized residual signal and the first target residual signal. A second residual quantization is performed on the second target residual signal at a second bit rate to generate a second quantized residual signal, where the first bit rate is different from the second bit rate.

Abstract: A method of training a natural language neural network comprises obtaining at least one constituency span; obtaining a training video input; applying a multi-modal transform to the video input, thereby generating a transformed video input; comparing the at least one constituency span and the transformed video input using a compound Probabilistic Context-Free Grammar (PCFG) model to match the at least one constituency span with corresponding portions of the transformed video input; and using results from the comparison to learn a constituency parser.

Abstract: Set forth is a motorized computing device that selectively navigates to a user according content of a spoken utterance directed at the motorized computing device. The motorized computing device can modify operations of one or more motors of the motorized computing device according to whether the user provided a spoken utterance while the one or more motors are operating. The motorized computing device can render content according to interactions between the user and an automated assistant. For instance, when automated assistant is requested to provide graphical content for the user, the motorized computing device can navigate to the user in order to present the content the user. However, in some implementations, when the user requests audio content, the motorized computing device can bypass navigating to the user when the motorized computing device is within a distance from the user for audibly rendering the audio content.

Abstract: Embodiments described herein provide systems and methods for data-to-text generation. The embodiments receive input data that includes a resource description framework (RDF) triples in an RDF graph. A data-to-text generation system generates position aware embeddings, including position embeddings, triple role embeddings, and tree-level embeddings. Using the position aware embeddings and the RDF graph, the data-to-text generation system generates a textual description for the RDF graph.

Abstract: There is a need for more effective and efficient predictive natural language summarization. This need can be addressed by, for example, solutions for performing predictive natural language summarization using a constrained optimization model. In one example, a method includes identifying one or more per-party utterance subsets in a multi-party call transcript; generating a plurality of eligible extractive summaries that comply with one or more optimization constraints; for each eligible extractive summary of the plurality of eligible extractive summaries, determining an overall summary utility measure; generating the optimal extractive summary based at least in part on each overall summary utility measure for an eligible extractive summary of the plurality of eligible extractive summaries; and performing one or more summary-based actions based at least in part on the optimal extractive summary.

Abstract: A sampling rate processing method performed by a computer device are disclosed. The method includes: obtaining a first audio signal recorded by a transmitting device, the first audio signal being recorded according to an initial sampling rate of the transmitting device; obtaining a second audio signal recorded by a receiving device during playing of the first audio signal, the second audio signal being recorded according to the initial sampling rate; determining a frequency response gain value of the receiving device according to a power spectrum of the first audio signal and a power spectrum of the second audio signal; determining a target sampling rate of the transmitting device according to the initial sampling rate and the frequency response gain value; and configuring the transmitting device to record audio signals according to the target sampling rate.

Abstract: In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.

Abstract: The present disclosure relates to automatically correcting mispronounced keywords during a conference session. More particularly, the present invention provides methods and systems for automatically correcting audio data generated from audio input having indications of mispronounced keywords during an audio/videoconferencing system. In some embodiments, the process of automatically correcting the audio data may require a re-encoding process of the audio data at the conference server. In alternative embodiments, the process may require updating the audio data at the receiver end of the conferencing system.

Abstract: Systems and methods for device functionality identification are disclosed. For example, a connected device may be coupled to a secondary device. A user may request operation of the connected device, and a system may determine that the connected device is of a given device type. Based on the connected device being of the given device type, the system may cause another device having an environmental sensor to send sensor data indicating environmental changes sensed by the sensor. The connected device may be operated and the sensor may sense environmental changes caused by operation of the connected device. When the sensed environmental changes indicate the device type of the secondary device, a recommendation to change the device type of the connected device to the device type of the secondary device may be provided to a user device associated with the connected device.

Abstract: A method and apparatus for controlling a device according to an embodiment of the present disclosure may be based on a speech feature of a user reflecting the Lombard effect so as to operate a device located far away from the user, among a plurality of electronic devices. As such, even when the user calls a device located far away from the user without any separate context information, speech recognition neural networks and weight calculation neural networks may be selected and used to operate the device located far away from the user, and reception of a speech signal of the user calling a device located far away from the user may be performed in an Internet of Things (IoT) environment using a 5G network.