Abstract: An incongruent headline detection system receives a request to determine a headline incongruence score for an electronic document. The incongruent headline detection system determines the headline incongruence score for the electronic document by applying a machine learning model to the electronic document. Applying the machine learning model to the electronic document includes generating a graph representing a textual similarity between a headline of the electronic document and each of a plurality of paragraphs of the electronic document and determining the headline incongruence score using the graph. The incongruent headline detection system transmits, responsive to the request, the headline incongruence score for the electronic document.

Abstract: A method of real-time noise reduction including generating spectral data using temporally localized spectral representations of a received audio signal, determining detection of voice by comparing first and second filtered data, and generating noise-reduced audio output by attenuating noise based on the determined detection of voice. The first and second filtered data are formed by attenuating temporal variations of the spectral data based on, respectively, a first timescale and a second timescale. A noise reduction system, comprising processing circuitry configured to execute a method of real-time noise reduction to generate an output that is transmitted via an output port of the noise reduction system. A noise-reduction microphone comprising a housing having a transducer coupled to a processor therein to execute a method of real-time noise reduction, and an output port. A non-transitory computer-readable medium having instructions to cause a processor to perform a method of real-time noise reduction.

Abstract: Generally discussed herein are devices, systems, and methods for generating a phrase that is confusing to a language classifier. A method can include determining, by the LC, a first classification score (CS) of a prompt indicating whether the prompt is a first class or a second class, predicting, based on the prompt and by a pre-trained language model (PLM), likely next words and a corresponding probability for each of the likely next words, determining, by the LC, a second CS for each of the likely next words, determining, by an adversarial classifier, respective scores for each of the likely next words, the respective scores determined based on the first CS of the prompt, the second CS of the likely next words, and the probabilities of the likely next words, and selecting, by an adversarial classifier, a next word of the likely next words based on the respective scores.

Abstract: A computer-implemented method to determine whether to introduce latency into an audio stream from a particular speaker includes an audio stream from a sender device. The method further includes providing, as input to a trained machine-learning model, the audio stream and a speech analysis score, information about one or more voice emotion parameters, and one or more voice emotion scores for a first user associated with the sender device, wherein the trained machine-learning model is iteratively applied to the audio stream and wherein each iteration corresponds to a respective portion of the audio stream. The method further includes generating as output, with the trained machine-learning model, a level of toxicity in the audio stream. The method further includes transmitting the audio stream to a recipient device, wherein the transmitting is performed to introduce a time delay in the audio stream based on the level of toxicity.

Abstract: A computer-implemented method for providing a voice interface system comprising: monitoring a first voice input during a training phase; monitoring manual inputs to a system during the monitoring the first voice input; storing training information associating the first voice input with the manual inputs; receiving a second voice input during an operational or runtime phase after the associating; identifying an instruction corresponding to the second voice input based on the training information; and executing the identified instruction.

Abstract: An apparatus for encoding audio information is provided. The apparatus for encoding audio information includes a selector for selecting a comfort noise generation mode from two or more comfort noise generation modes depending on a background noise characteristic of an audio input signal, and an encoding unit for encoding the audio information, wherein the audio information includes mode information indicating the selected comfort noise generation mode.

Abstract: A method for more efficient structural parsing of binary representations of text based objects within a data distribution system. Clients subscribe to a topic maintained by the data distribution system server that publishers can publish to. Clients receive an original binary representation of a text based object describing the state of the topic to which the client is subscribed. In response to the state of the topic changing at the data distribution system server, clients receive a binary delta representing the change of the state of the topic. Based on the received binary representation and the binary delta, clients calculate an updated binary representation of the text based object. Using the original binary representation, the updated binary representation, and the binary delta, the client generates a structural delta representing the structural differences between data structures of the original text based object and data structures of the updated text based object.

Abstract: Presented herein are techniques for augmenting a speech recognition engine. According to the disclosed techniques, audio data is obtained as part of an automatic speech recognition session. Speech hints are also obtained as part of the automatic speech recognition session. A dynamic language model is generated from the speech hints for use during the automatic speech recognition session. A combined language model is then generated from the dynamic language model and a static language model. Finally, the audio data is converted to text using the combined language model as part of the automatic speech recognition session.

Abstract: An audio signal processing method includes measuring a voice signal, wherein the measurement performed by an audio system including first through third sensors. Measuring the voice signal produces first through third audio signals by the first through third sensors, respectively. The audio signal processing method further includes: producing an output signal by using the first audio signal, the second audio signal and the third audio signal, wherein the output signal corresponds to: the first audio signal below a first crossing frequency, the second audio signal between the first crossing frequency and a second crossing frequency, the third audio signal above the second crossing frequency. The first crossing frequency is lower than or equal to the second crossing frequency, wherein the first crossing frequency and the second crossing frequency are different for at least some operating conditions of the audio system.

Abstract: Described herein is a method for generating a modified bitstream on a source device, wherein the method includes the steps of: a) receiving, by a receiver, a bitstream including coded media data; b) generating, by an embedder, payload of additional media data and embedding the payload in the bitstream for obtaining, as an output from the embedder, a modified bitstream including the coded media data and the payload of the additional media data; and c) outputting the modified bitstream to a sink device. Described is further a method for processing said modified bitstream on a sink device. Described are moreover a respective source device and sink device as well as a system of a source device and a sink device and respective computer program products.

Abstract: Disclosed are an electronic device capable of efficiently performing speech recognition and natural language understanding and a method for controlling thereof. The electronic device includes: a microphone; a non-volatile memory configured to store virtual assistant model data comprising data that is classified according to a plurality of domains and data that is commonly used for the plurality of domains; a volatile memory; and a processor configured to: based on receiving, through the microphone, a trigger input to perform speech recognition for a user speech, initiate loading the virtual assistant model data from the non-volatile memory into the volatile memory, load, into the volatile memory, first data from among the data classified according to the plurality of domains and, while loading the first data into the volatile memory, load at least a part of the data commonly used for the plurality of domains into the volatile memory.

Abstract: Systems and methods for a computerized interactive voice companion include functionality that receives audio of a user's voice as the user is speaking; detects a tone and/or other relevant aspects associated with the content of the user's voice based on the audio of the user's voice as the user is speaking and determines, as the user is speaking, a response to the user speaking based on the detected tone and/or other relevant aspects associated with the content of the user's voice of the user's voice. The computerized interactive voice companion system, then orally or visually provides the response to the user automatically in real-time as a reply to the user speaking. The system may then continue the conversation based on continuing to detect the mood of the user as they speak and basing responses on this, as well as other recent user behavior detected to be relevant to the conversation.

Abstract: A method for controlling a motion tracking system, including the steps of: digitally processing sound waves detected by a plurality of microphones so as to detect a voice of a user and estimate a first direction of the user; digitally processing electromagnetic waves captured by antennas so as to detect data packets transmitted to a computing apparatus by sensors and estimate second directions of each sensor; digitally averaging the second directions so as to provide an average direction for the sensors; digitally computing a difference between the first direction and the average direction; and starting to digitally track motion of the user based on measurements of each sensor when the computed difference does not exceed a predetermined difference threshold.

Abstract: A computer-implemented process is programmed to process a source input, determine text enhancements, and present the text enhancements to apply to the sentences dictated from the source input. A text processor may use machine-learning models to process an audio input to generate sentences in a presentable format. An audio input can be processed by an automatic speech recognition model to generate electronic text. The electronic text may be used to generate sentence structures using a normalization model. A comprehension model may be used to identify instructions associated with the sentence structures and generate sentences based on the instructions and the sentence structures. An enhancement model may be used to identify enhancements to apply to the sentences. The enhancements may be presented alongside sentences generated by the comprehension model to provide the user an option to select either the enhancements or the sentences.

Abstract: Techniques for virtual assistant execution of ambiguous commands is provided. A voice instruction from a user may be received at a virtual assistant. The voice instruction may request the virtual assistant to perform a command. The command that is most likely being requested by the voice instruction from the user is identified. An ordered set of actions to execute when performing the command may be retrieved. Each action of the ordered set of actions may indicate if the action is reversible. Each action of the ordered set of actions may be executed in order until a not reversible action is reached or no further actions are in the ordered set of actions.

Abstract: The present disclosure relates to a text-to-speech synthesis method using machine learning based on a sequential prosody feature. The text-to-speech synthesis method includes receiving input text, receiving a sequential prosody feature, and generating output speech data for the input text reflecting the received sequential prosody feature by inputting the input text and the received sequential prosody feature to an artificial neural network text-to-speech synthesis model.

Abstract: A voice control interactive system and method to provide a hands-free operation for the operator to monitor and control multiple conveyors in a warehouse. The system comprises a first computing device and a second computing device. The first computing device receives an audio signal generated by a second computing device and generates a control signal and a response signal in response to the audio signal. The audio signal comprises information relating to a verbal command spoken by an operator associated with the second computing device. The response signal comprises information relating to a response for the verbal command, wherein the information is generated based on a location of the second computing device. The control signal comprises information to control a conveyor.

Abstract: Various embodiments of a system and associated method for anonymization of text without losing semantic utility of text by extracting a latent embedding representation of content with respect to a given task and by learning an optimal strategy for text embedding manipulation to satisfy both privacy and utility requirements are disclosed herein. In particular, the system balances private attribute obfuscation with retained semantic utility.

Abstract: An illustrative collaboration space provider system provides a virtual collaboration session that allows for audio communication between a user and one or more other users virtually located within a virtual collaboration space. The user is represented by an avatar located at an avatar location within the virtual collaboration space. The collaboration space provider system receives user input from the user, the user input representative of a voice origination location that is within the virtual collaboration space and is distinct from the avatar location. During the virtual collaboration session, the collaboration space provider system simulates propagation within the virtual collaboration space of a voice communication spoken by the user. The propagation of the voice communication is simulated to originate from the voice origination location and not from the avatar location. Corresponding methods and systems are also disclosed.

Abstract: Example techniques involve suppressing a wake word response to a local wake word. An example implementation involves a playback device receiving audio content for playback by the playback device and providing a sound data stream representing the received audio content to a voice assistant service (VAS) wake-word engine and a local keyword engine. The playback device plays back a first portion of the audio content and detects, via the local keyword engine, that a second portion of the received audio content includes sound data matching one or more particular local keywords. Before the second portion of the received audio content is played back, the playback device disables a local keyword response of the local keyword engine to the one or more particular local keywords and then plays back the second portion of the audio content via one or more speakers.