Abstract: A computer-implemented method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. Here, the ASR model includes a causal encoder and a decoder. The method also includes generating, by the causal encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by the decoder, a first probability distribution over possible speech recognition hypotheses. Here, the causal encoder includes a stack of causal encoder layers each including a Recurrent Neural Network (RNN) Attention-Performer module that applies linear attention.

Abstract: An apparatus including circuitry configured to obtain a defocus direction; process a spatial audio signal that represents an audio scene to generate a processed spatial audio signal that represents a modified audio scene based on the defocus direction, so as to control relative deemphasis in, at least in part, a portion of the spatial audio signal in the defocus direction relative to at least in part other portions of the spatial audio signal; and output the processed spatial audio signal, wherein the modified audio scene based on the defocus direction enables the deemphasis in, at least in part, the portion of the spatial audio signal in the defocus direction relative to at least in part other portions of the spatial audio signal.

Abstract: Methods and systems are provided for customizing an action. In some implementations, voice input is received from a user and a context is determined from the voice input. Potential contextual data is identified based on the context and the voice input. A level of confidence is determined for an association of the potential contextual data and the context. An action is performed based on the voice input, the potential contextual data, and the level of confidence. The potential contextual data is used to customize the action.

Abstract: A speech recognition system comprises: an input, for receiving an input signal from at least one microphone; a first buffer, for storing the input signal; a noise reduction block, for receiving the input signal and generating a noise reduced input signal; a speech recognition engine, for receiving either the input signal output from the first buffer or the noise reduced input signal from the noise reduction block; and a selection circuit for directing either the input signal output from the first buffer or the noise reduced input signal from the noise reduction block to the speech recognition engine.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automated prototyping of a topic model. An example method includes a data manipulation engine ingesting and pre-processing source data from a set of data sources, a feature extraction engine that thereafter transforms the pre-processed data into a set of numeric representations of the pre-processed data, and an autonomous model generator that automatically generates a trained topic model using the set of numeric representations. Embodiments further enable visualization of topic model output, which permits a user to easily consume and utilize information from a topic model for any number of purposes.

Abstract: In an embodiment, a programmed computer system implemented via client-server Software as a Service (SaaS) techniques provides an interactive user interface for identifying specific portions of a digital document susceptible for review and improvement. A server computer may receive a representation of a digital document, such as an email, comprising words arranged into sentences. An embodiment may tokenize a set of all sentences comprising the sequence of sentences into a document-specific vocabulary, then compute a corresponding first and second score for each sentence of the sequence of sentences. The first score may represent a calculated probability of semantic importance of the corresponding sentence to an overall meaning of the digital document. The second score may represent a calculated likelihood that the corresponding sentence will be read by a future reader of the digital document. An embodiment may identify key sentences using the first scores and second scores.

Abstract: A system for intelligent editing of legal documents. The system includes a computing device. The computing device is configured to access a plurality of legal source texts from a plurality of legal sources, generate a score for each of the plurality of legal source texts, train a natural language processing model as a function of the scored legal source texts and a first machine-learning process, receive user inputted legal text from a user device being operated by a human user to create a user legal document, analyze the user inputted legal text using the natural language processing model, suggest, as a function of the analyzing, a modification to a target text of the user inputted legal text, and generate a score for a modified user legal document. A method for intelligent editing of legal documents is also provided.

Abstract: An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.

Abstract: A method of processing speech includes: providing a first set of audio data having audio features in a first bandwidth; down-sampling the first set of audio data to a second bandwidth lower than the first bandwidth; producing, by a high frequency reconstruction network (HFRN), an estimate of audio features in the first bandwidth for the first set of audio data, based on at least the down-sampled audio data; inputting, into the HFRN, a second set of audio data having audio features in the second bandwidth; producing, by the HFRN, based on a second set of audio data having audio features in the second bandwidth, an estimate of audio features in the first bandwidth for the second set of audio data; and training a speech processing system (SPS) using the estimates of audio features in the first bandwidth for the first and second sets of audio data.

Abstract: Voice-based interaction with video content being presented by a media player application is enhanced through the use of an automated assistant capable of identifying when a spoken utterance by a user is a request to playback a specific scene in the video content. A query identified in a spoken utterance may be used to access stored scene metadata associated with video content being presented in the vicinity of the user to identify one or more locations in the video content that correspond to the query, such that a media control command may be issued to the media player application to cause the media player application to seek to a particular location in the video content that satisfies the query.

Abstract: A computer-implemented method for generating natural language explanations of product formulations includes implementing a causal-based formulation network model within a web-based graphical, activating a target causal path of the causal-based formulation network model based on subscriber input; constructing a formulation impact explanation prompt based on a formulation outcome node and a sequence of interconnected formulation parameter nodes of the target causal path; generating, by a large language model, a natural language explanation of the target causal path based on an input of the formulation impact explanation prompt; and surfacing, by the web-based graphical user interface, the natural language explanation of the target casual path.

Abstract: The deep semantic feature based few-shot intent recognition method for air traffic control instructions, belonging to the technical field of air traffic control; the method of the present invention solves technical problems in the prior art relating to incomplete and unreliable security situation awareness and semantic representation due to poor intent recognition ability for instructions with a small sample size, as well as difficulty in improving the applicability of models while keeping a small amount of labeled language information; the present invention uses the one obtained air traffic control instruction set to construct a domain language model and adopts an unsupervised learning method to enhance the mining and representation of deep-level features of air traffic control air-ground communication, thereby further improving the reliability of safety situation awareness and semantic representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing speech using a spiking neural network acoustic model implemented on a neuromorphic processor are described. In one aspect, a method includes receiving, a trained acoustic model implemented as a spiking neural network (SNN) on a neuromorphic processor of a client device, a set of feature coefficients that represent acoustic energy of input audio received from a microphone communicably coupled to the client device. The acoustic model is trained to predict speech sounds based on input feature coefficients. The acoustic model generates output data indicating predicted speech sounds corresponding to the set of feature coefficients that represent the input audio received from the microphone. The neuromorphic processor updates one or more parameters of the acoustic model using one or more learning rules and the predicted speech sounds of the output data.

Abstract: Systems and methods for creating a view of an environment are disclosed. Exemplary implementations may: receive parameters and measurements from at least two of one or more microphones, one or more imaging devices, a radar sensor, a lidar sensor, and/or one or more infrared imaging devices located in a computing device; analyze the parameters and measurements received from the multimodal input; generate a world map of the environment around the computing device; and repeat the receiving of parameters and measurements from the input devices and the analyzing steps on a periodic basis to maintain a persistent world map of the environment.

Abstract: A meeting summarization method, system, and computer program product, include compiling notes from a meeting between a plurality of users and providing a single document that summarizes the meeting based on the compiled notes.

Abstract: The invention relates to a method and a system for improving performance of text summarization and has an object of improving performance of a technique for generating a summary from a given paragraph. According to the invention to achieve the object, a method for improving performance of text summarization includes: an a step of generating an embedding vector by vectorizing a natural language-based context; a b step of generating a graph using the embedding vector and calculating a first likelihood of each of at least one node included in the graph; a c step of generating a second likelihood by assigning a weight to the first likelihood according to a result of comparing at least one node included in the graph with the context; and a d step of calculating a third likelihood for all candidate paths present in the graph based on the second likelihood, selecting a path having a highest third likelihood, and generating a summary based on the path.

Abstract: A server accesses a natural language query. The server facilitates a mapping of the natural language query to a vector using a query-to-vector engine. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.

Abstract: Systems and methods for content framing monitoring and intervention are disclosed. In one example, a method includes receiving, at a computing device, first text conveying first information, performing natural language processing of the first text, determining a first framing of the first information based on the natural language processing of the first text, determining second text conveying the first information with a second framing, different from the first framing, and outputting the second text on an electronic display.

Abstract: Disclosed implementations include systems, methods, and apparatus that process multiple, disparate streams of data, determine correlations and relationships between the data and provide natural language responses that provide insights for events or activities that have occurred and foresights for events or activities that are forecasted to occur. The disclosed implementations include a model that understands data statistics and provides both insights and foresights that are backed with statistical support that can be presented to and understood by operators. Still further, the disclosed implementations are capable of operating at edge locations that may be frequently or permanently disconnected from conventional or cloud based systems.

Abstract: Implementations of the disclosure provide a method, system and computer program product for model localization. In an implementation of the disclosure, a method for model localization includes parsing a model to identify translatable terms, generating a seed file associating each of the translatable terms with a corresponding tag and replacing each translatable term in the model with a corresponding tag and submitting each of the translatable terms to machine translation for a target language to produce a different translation file mapping each tag from the seed file with a translated term in the target language of a corresponding one of the translatable terms. Then, the model may be deployed in a data analytics application using the different translation file to dynamically translate each translatable term into a corresponding translated term within a user interface to the data analytics application.