Abstract: A computer-implemented method of performing text-to-text transformation includes performing a text transformation operation on an original input text of a specific task to generate a plurality of transformed text. A task-specific performance metric that measures an operation of the specific task is applied to each one of the plurality of transformed text. Each of the plurality of transformed text are paired with the task-specific performance metric. A training dataset is updated to include each pairing of the plurality of transformed text with the task-specific metric.

Abstract: In an embodiment of the present disclosure, a method of simultaneously identifying intent and slots in a voice assistant command includes tokenizing, into a plurality of tokens, a current utterance of a user of a device comprising the voice assistant command, prepending the plurality of tokens with a previous utterance and a separation token, obtaining, using a transformer-based machine learning model, one or more predictions for the voice assistant command from the prepended plurality of tokens, aligning, according to one or more constraints, the at least one of the flag prediction, the goal prediction, and the sub-goal prediction, providing, to the device, the identified intent and the identified slots based on the intent prediction and the aligned at least one of the flag prediction, the goal prediction, and the sub-goal prediction, causing the device to perform the voice assistant command according to the identified intent and the identified slots.

Abstract: A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes.

Abstract: A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag. The high frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums for utilizing context information to create decoding feedback information to improve decoder accuracy and/or performance. In some examples, the context information is from layers of a network stack above the layers in which the decoders are present. The context information may be or be based upon information about previously received and decoded data and/or information about the sender to provide decoding feedback information to the decoder that is used either to correct a previous decoding error or to inform the decoder on which of a plurality of decoding choices is more likely to be correct. This may increase decoding performance by decreasing errors and in some examples, reducing the complexity of choices by eliminating certain decoding possibilities and thus increasing decoder efficiency.

Abstract: A system and methods for latent contextual threading for personalized dialogue through geometric manifold-based conversation management. The system maintains a personalized cognitive manifold as a geometric manifold in latent space that encodes user-specific dialogue patterns as navigable geometric structures. Multiple dialogue contexts are maintained as geometric trajectories within the manifold, with dialogue responses generated through manifold traversal rather than discrete context retrieval. A bidirectional adaptation system modifies the manifold's geometric structure based on user interactions. The system preserves dialogue continuity across session boundaries by serializing manifold geometry during session termination and restoring geometric positioning during session resumption. Dialogue coherence is evaluated through geometric analysis including curvature calculations and geodesic deviation measurements.

Abstract: A method and a system for interacting with one or more computer resource assets at a location. The system includes a processor, a storage, and an interface suite including a first interface configured to communicate with a user device and a second interface configured to interact with at least one computing resource asset at the location.

Abstract: In one aspect, a method includes detecting a fingerprint match between query fingerprint data representing at least one audio segment within podcast content and reference fingerprint data representing known repetitive content within other podcast content, detecting a feature match between a set of audio features across multiple time-windows of the podcast content, and detecting a text match between at least one query text sentences from a transcript of the podcast content and reference text sentences, the reference text sentences comprising text sentences from the known repetitive content within the other podcast content. The method also includes responsive to the detections, generating sets of labels identifying potential repetitive content within the podcast content. The method also includes selecting, from the sets of labels, a consolidated set of labels identifying segments of repetitive content within the podcast content, and responsive to selecting the consolidated set of labels, performing an action.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating responses to multi-order text queries using a context orchestration engine. For example, the disclosed systems generate context-defining query subcomponents from a multi-order text query, where the context-defining query subcomponents indicate contextual data sources pertaining to their respective portions of the multi-order text query. In addition, the disclosed systems provide or transmit the context-defining query subcomponents to a large language model for domain-specific computer code pertaining to each respective context-defining query subcomponent. The disclosed systems can further execute the generated computer code for each context-defining query subcomponent to access indicated contextual data sources for generating component-specific results. The disclosed systems can also generate a multi-order result to the multi-order text query from the component-specific results.

Abstract: A computer-implemented method and computer system improvements for automatically generating a summary note related to a digitally-recorded interlocutor conversation, such as a chat transcript, including accessing a data corpus having at least one digitally-recorded conversation of text-based interlocutory conversation(s) including at least one labeled value, extracting at least one Conversation Feature from the labeled value(s), creating at least one Summary Feature from the extracted Conversation Feature(s), generating at least one Summary Note by combining one or more Narrative Structures with the Summary Feature(s), and digitally outputting the at least one Summary Note.

Abstract: A method of suppressing noise may include receiving a sequence of audio frames representing a multi-channel audio signal. The method may include determining a likelihood of speech in a first audio frame of the sequence of audio frames based on a Gaussian mixture model. Further, the method may include generating a first audio signal based on the likelihood of speech in the first audio frame and a second audio signal representing a first speech component of a second audio frame. The second audio frame follows the first audio frame in the sequence of audio frames. The method may also include determining, using a neural network model, a likelihood of speech in the second audio frame based on the first audio signal, and filtering a noise component of the second audio frame based at least in part on the likelihood of speech in the second audio frame.

Abstract: This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques that combine statistical signal processing with neural network inferencing. In some aspects, a speech enhancement system may include a linear filter, a deep neural network (DNN), and a nonlinear post-filter. The linear filter and the nonlinear post-filter are configured to suppress noise in audio signals using statistical signal processing techniques. More specifically, the linear filter denoises an input audio signal based on a temporal correlation between successive frames of the audio signal. The DNN infers a speech signal and a noise signal (representing a speech component and a noise component, respectively, of the audio signal) based on the denoised audio signal.

Abstract: The present disclosure is generally directed to a tangible, non-transitory machine-readable medium that includes machine-readable instructions that, when executed, cause processing circuitry to receive a first indication of multimedia content and a second indication of whether the multimedia content is to be transcribed. The instructions cause the processing circuitry to send content generated from the multimedia content for transcription. The content includes an identifier associated with the multimedia content. Additionally, the instructions cause the processing circuitry to send a request for the content to be transcribed. The request includes or is indicative of the identifier. Moreover, the instructions cause the processing circuitry to receive a transcript for at least a portion of the content and generate transcript metadata that includes timing data and is indicative of text of the transcript.

Abstract: The techniques disclosed herein manage computing environments associated with radio access networks using a natural language interface. This is achieved through utilizing natural language processing to analyze user generated inputs and generate robust large language model queries. In various examples, the queries can include radio access network documentation, diagnostic data, and past interactions to provide custom context to the large language model. Accordingly, the query can cause the large language model to generate an operation sequence comprising a plurality of commands to interface with a resource management tool and control computing resources and supporting components. In this way, the present techniques can alleviate the technical burden on end users and minimize the risk of errors.

Abstract: Systems and methods include acquisition of operation data associated with a plurality of operations, generate a set of feature values for each operation based on associated operation data, determine a first set of the operations associated with an error and, for each operation of the first set, automatically determine a rejection reason based on operation data associated with the operation, determine a rejection label associated with the rejection reason, and associate the rejection label with the set of feature values generated for the operation.

Abstract: A method, computer program product, and computer system for generation of training examples for training an automatic speech recognizer. Embodiments of the present invention can receive a training dataset of original audio signals and generate training examples for training an automatic speech recognizer based, at least in part, on a constructed imperceptible space for an original audio signal of the original audio signals and adversarial audio examples in the constructed imperceptible space. Embodiments of the present invention can then generate an imperceptible and adversarial audio example to an adversarial trainer for the automatic speech recognizer.

Abstract: A management system (1090) includes an air conditioner (1062), a sound input unit (1064), an operation sound analyzing unit (1016), a first preprocessing unit (1011), a command extracting unit (1014), and a control unit (1017). The sound input unit (1064) is provided in a room where the air conditioner is provided and obtains sound data (SD). The operation sound analyzing unit (1016) analyzes the sound data by a predetermined analysis method so as to obtain a determination result (RE) regarding whether an abnormality exists in the air conditioner or details of the abnormality. The first preprocessing unit (1011) performs first preprocessing on the sound data. The command extracting unit (1014) extracts a command (CM) of a user from the sound data that has been subjected to the first preprocessing. The control unit (1017) controls the air conditioner on the basis of the command.

Abstract: A knowledge accelerator platform automatically builds and maintains a knowledge index of assets stored across a plurality of sources to allow consolidated searching of assets in the plurality of sources. The platform automatically processes the assets to extract metadata information and record labels for each asset in the index, without storing the content of an asset itself in the index, where a semantic dictionary maps labels to terminology in different domains. The knowledge accelerator platform further provides an internal user interface that allows internal users to evaluate the knowledge contained in the assets at a high level and thus identify knowledge gaps that can be mitigated, and promote quality assets within targeted user communities. The knowledge accelerator platform also provides an external user interface that recommends assets to users based on various factors such as their experience, status, and domain interests.

Abstract: Method, apparatus, and non-transitory storage medium for training a deep neural-network model jointly for acoustic echo suppression and acoustic howling suppression are provided. The method may include generating a teacher speech signal for training the deep neural-network model based on a input speech from a speech system and at least one reference signal. The deep neural-network model is trained jointly for both acoustic echo suppression and acoustic howling suppression by using the teacher speech signal and a correlation loss. During training of the deep neural-network model, the training task formulates a recurrent feedback suppression process as an instantaneous speech separation task using the teacher-forced training strategy.

Abstract: Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.