Abstract: Systems, computer program products, and methods are described herein for digital voice data processing and authentication. The present invention is configured to receive a user interaction comprising a digital audio signal and capture a first audio segment and a second audio segment of the digital audio signal. The first audio segment and the second audio segment are plotted into corresponding first and second plots. The first and second plots are compared, wherein comparing comprises subtracting the first plot from the second plot to form a difference plot. A quantity of outlier peaks is determined, then an artificial user probability is assigned to the user interaction, wherein the artificial user probability is low if the quantity of outlier peaks is greater than a predetermined outlier peak threshold. The artificial user probability is then displayed on a user interface of an endpoint device.

Abstract: Technology is disclosed for programmatically determining an event of interest that is specific to a user, and generating, based on user-meeting data, an enriched playback timeline that includes the event of interest on a graphical user interface (GUI). To determine the event of interest, this disclosure provides technologies to determine one or more meeting data features based on meeting data associated with a meeting. Based on the one or more meeting data features, this disclosure includes determining an event of interest. The event of interest may include, for example, an indication of whether a person was mentioned, an indication of whether a question was asked, an indication of whether a keyword was mentioned, an indication of whether a topic was covered, and so forth. From these events of interest, a GUI that includes an enriched meeting playback timeline that includes an indication of the event of interest may be generated.

Abstract: A method for adjusting the volume of an electronic device, according to an embodiment, may comprise: adjusting, based on a user's voice being input into a first electronic device, the output volume of a second electronic device based on a second volume adjustment amount, determined based on the relative difference between the volume of the user's voice and the volume of the second electronic device measured in the first electronic device, and/or a third volume adjustment amount, input through a volume adjustment interface; adjusting, based on a conversation partner's voice being output from the first electronic device, the output volume of the first electronic device based on a first volume adjustment amount, determined based on the volume of the second electronic device measured in the first electronic device, and/or the third volume adjustment amount; and adjusting, based on the user and the conversation partner speaking simultaneously, the output volume of the second electronic device based on the first vo

Abstract: A named-entity recognition (NER) model detects named entities with types that correspond to protected health information (PHI) in potentially sensitive documents. The NER model is trained to detect named entities corresponding to both personally identifiable information (PII) and medical terms. Output of the NER model is preprocessed as input to a random forest classifier that outputs a verdict that documents comprise sensitive data. The verdict is interpretable via high confidence named entities detected by the NER model that led to the verdict.

Abstract: To perform a Named Entity Recognition (NER) operation that accounts for semantic relationships between entity and document text while remaining scalable, entity text and text from portions of documents are encoded separately, in some cases using different encoders such as when the document text also includes layout information. A cross-attention module is then used to determine a weighted embedding for each document embedding, based on the document embedding and each entity embedding. The weighted embeddings include weights indicative of semantic relationships between the document text and the text of each entity. Correspondence between the weighted embeddings and the corresponding document embedding is then used to determine scores representing the probability that an entity is referenced in a portion of a document. Because the weighted embeddings are used, these scores account for semantic relationships between entity and document text.

Abstract: Provided is a computer-implemented method, system, and computer program product for generating a goal-oriented dialogue from a grounding document. A processor may analyze a corpus of text. The processor may identify, based on the analyzing, one or more semantic structures that can be used to simulate a dialogue. The processor may generate, based on the identifying, a simulated dialogue, the simulated dialogue including one or more utterances from a simulated agent and one or more utterances from a simulated user to form a dialogue flow.

Abstract: Systems and methods for providing user interfaces to converse with a corpus of electronic documents via a large language model are disclosed. Exemplary implementations may: present a user interface configured to obtain entry of user input from a user to select one or more documents to be provided as input to a large language model for an individual conversation; responsive to selection of the individual conversation, provide an individual query as a prompt to the large language model; obtain and present an individual reply from the large language model; determine an individual document from the one or more documents that is relevant to the individual reply; present the individual document in a particular portion of the user interface; and/or perform other steps.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for interactive decoding of a word sequence.

Abstract: An audio processing method includes obtaining a first audio signal corresponding to a first frame; extracting a first feature vector by inputting the first audio signal to a first neural network; obtaining a temporal correlation vector representing a similarity between the first feature vector and at least one second feature vector extracted from at least one second audio signal corresponding to at least one second frame that is temporally before the first frame; and classifying a scene of the first audio signal by inputting the first feature vector, the at least one second feature vector, and the temporal correlation vector to a second neural network.

Abstract: Systems and methods for key-phrase extraction are described. The systems and methods include receiving a transcript including a text paragraph and generating key-phrase data for the text paragraph using a key-phrase extraction network. The key-phrase extraction network is trained to identify domain-relevant key-phrase data based on domain data obtained using a domain discriminator network. The systems and methods further include generating meta-data for the transcript based on the key-phrase data.

Abstract: Processing sound signals acquired by at least one microphone, to locate a sound source emitting from a plurality of discrete positions at respective discrete points in time, in a space comprising at least one planar reflective surface. The method includes: obtaining: a first vector u ? 0 ( k ) determining a direction of a first acoustic path, direct between the source and the microphone, a second vector u ? n ( k ) representing a second acoustic path resulting from a specular reflection and arriving at the microphone, and a delay ? n ( k ) of second path at the microphone, compared to the direct path; exploiting a property of the specular reflection according to which a Euclidean distance between two positions of the source at two discrete points in time is equal to a Euclidean distance between two respective positions of images of the source and derived from one or more same reflections, respectively at said two discrete points in time.

Abstract: A computer-implemented system personalizes virtual advisors for immersive healthcare by creating virtual medical and spiritual avatars that resemble trusted authority figures using deepfake technology and multimodal deep neural networks. The virtual medical advisor tailors guidance by analyzing unstructured electronic health record data with natural language processing and BERT-based techniques while adapting its communication based on real-time physiological data from sensors like EEG and photoplethysmography. Concurrently, the virtual spiritual advisor offers faith-based counseling by factoring in user-declared spiritual preferences and sacred text analysis weighted for doctrinal considerations. Additional features include gamification with cryptocurrency tokens or NFTs for health activities, blockchain-based audit trails for HIPAA compliance, and federated learning with differential privacy.

Abstract: A system and process for pre-distorting TV shows and/or movie media enables digital transmission of the media via MPEG4/AC3 (or AAC) or MPEG4/AC4 codec for broadcast or streaming over the Internet with enhanced speech intelligibility. Processing of the entire media file is performed using pre-distortion techniques and algorithms including NN models (which includes DNN, RNN, CNN, and similar NN models) that are trained on perceptual codec induced noise, quantization noise, dynamic power level adjustment, frequency response adjustment, pitch and glottal impulse response adjustment, and other techniques. The pre-distortion process is iterative, and all combinations of pre-distortions to combat perceptual codec noise are attempted, and the result scored by an automatic speech recognition engine. The best speech recognition results and highest intelligibility scores are considered to indicate the best pre-distortion to be applied.

Abstract: Provided are a speech translation method, a device, and a storage medium. The method includes: extracting, through an encoder of an end-to-end speech translation model, the semantic feature of a to-be-processed speech; decoding, through a decoder of the end-to-end speech translation model, a source language text corresponding to the semantic feature from the semantic feature; decoding, through the decoder of the end-to-end speech translation model, the semantic feature according to the source language text to obtain a text sequence corresponding to the semantic feature; and splitting the text sequence to obtain a target language text corresponding to the to-be-processed speech.

Abstract: Voice command recognition and natural language recognition are carried out using an accelerometer that senses signals from the vibrations of one or more bones of a user and receives no audio input. Since word recognition is made possible using solely the signal from the accelerometer from a person's bone conduction as they speak, an acoustic microphone is not needed and thus not used to collect data for word recognition. According to one embodiment, a housing contains an accelerometer and a processor, both within the same housing. The accelerometer is preferably a MEMS accelerometer which is capable of sensing the vibrations that are present in the bone of a user as the user is speaking words. A machine learning algorithm is applied to the collected data to correctly recognize words spoken by a person with significant difficulties in creating audible language.

Abstract: An aspect of the present disclosure relates to processing audio comprising decoding a first bitstream (b1) to obtain decoded immersive audio content (A), decoding a second bitstream (bp) to obtain pose information (P, V, V?) associated with a user of a lightweight processing device, determining a first head-pose (P?) based on the pose information, providing a downmix representation (Dmx) of the immersive audio content (A) corresponding to the first head pose (P?), rendering a set of binaural representations (BINn) of the immersive audio content (A), wherein the binaural representations correspond to a second set of head poses (Pn), computing reconstruction metadata (M) to enable reconstruction of the set of binaural representations from the downmix representation (Dmx), the metadata (M) including the first head pose (P?), and encoding the downmix representation (Dmx) and the reconstruction metadata (M) in a third bitstream (b2).

Abstract: A method includes inserting a set of canary text samples into a corpus of training text samples and training an external language model on the corpus of training text samples and the set of canary text samples inserted into the corpus of training text samples. For each canary text sample, the method also includes generating a corresponding synthetic speech utterance and generating an initial transcription for the corresponding synthetic speech utterance. The method also includes rescoring the initial transcription generated for each corresponding synthetic speech utterance using the external language model. The method also includes determining a word error rate (WER) of the external language model based on the rescored initial transcriptions and the canary text samples and detecting memorization of the canary text samples by the external language model based on the WER of the external language model.

Abstract: Methods and systems for performing a natural language processing task include identifying hypernym/hyponym relations in a depth-wise ontology and identifying synonymy relations in a breadth-wise ontology. The depth-wise ontology and the breadth-wise ontology are combined into a combined ontology using the identified hypernym/hyponym relations and the identified synonymy relations. Enhanced hypernym/hyponym relations are embedded using the combined ontology. A natural language processing task is performed using the enhanced hypernym/hyponym relations and the combined ontology.

Abstract: Hot word free adaptation, of one or more function(s) of an automated assistant, responsive to determining, based on gaze measure(s) and/or active speech measure(s), that a user is engaging with the automated assistant. Implementations relate to various techniques for mitigating false positive occurrences of and/or false negative occurrences, of hot word free adaptation, through utilization of personalized parameter(s) for at least some user(s) of an assistant device. The personalized parameter(s) are utilized in determining whether condition(s) are satisfied, where those condition(s), if satisfied, indicate that the user is engaging in hot word free interaction with the automated assistant and result in adaptation of function(s) of the automated assistant.

Abstract: Systems and methods for dialogue response prediction can leverage a plurality of machine-learned language models to generate a plurality of candidate outputs, which can be processed by a dialogue management model to determine a predicted dialogue response. The plurality of machine-learned language models can include a plurality of experts trained on different intents, emotions, and/or tasks. The particular candidate output selected may be selected by the dialogue management model based on semantics determined based on a language representation. The language representation can be a representation generated by processing the conversation history of a conversation to determine conversation semantics.